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1420 1421 1422 1423 1424 1425 1426 1427 1428 | # SPDX-FileCopyrightText: © 2025 Tenstorrent USA, Inc.
# SPDX-License-Identifier: Apache-2.0
"""Tests for Penalties1D module."""
from types import SimpleNamespace
import pytest
import torch
import ttnn
from models.common.modules.lazy_buffer import LazyBuffer
from models.common.modules.sampling import penalties_1d as penalties_module
from models.common.modules.sampling.penalties_1d import (
Penalties1D,
Penalties1DConfig,
PenaltyAccumulator,
PenaltyParams,
_materialize,
_resolve_penalties1d_config,
)
from models.common.utility_functions import comp_pcc
# 1D module suites target the T3K; skip when the host system is a Galaxy.
pytestmark = pytest.mark.usefixtures("skip_on_galaxy_system")
# ---------------------------------------------------------------------------
# Model name constants (match test_mlp_1d.py naming convention)
# ---------------------------------------------------------------------------
LLAMA_1B = "meta-llama/Llama-3.2-1B-Instruct"
LLAMA_3B = "meta-llama/Llama-3.2-3B-Instruct"
LLAMA_8B = "meta-llama/Llama-3.1-8B-Instruct"
LLAMA_11B = "meta-llama/Llama-3.2-11B-Vision-Instruct"
LLAMA_70B = "meta-llama/Llama-3.3-70B-Instruct"
MISTRAL_7B = "mistralai/Mistral-7B-Instruct-v0.3"
MIXTRAL_8X7B = "mistralai/Mixtral-8x7B-v0.1"
QWEN25_72B = "Qwen/Qwen2.5-72B-Instruct"
QWEN3_32B = "Qwen/Qwen3-32B"
_slow = pytest.mark.slow
def _list_collected_penalty_cases() -> list[pytest.param]:
"""
Collected from TTTv1 demo runs (Phase B of test_case_collection.md).
Each entry is:
(mesh_shape, vocab_size, batch_size, seq_len,
presence, frequency, repetition, pcc, hf_model_name)
Source CSVs: sampling_generator_config_collected.csv,
sampling_generator_params_collected.csv,
penalties_prompt_tokens_collected.csv
Deduplicated by (topology, vocab, batch, seq_len, penalties_active).
"""
# fmt: off
return [
# --- (1,1) Mistral7B v32768 ---
pytest.param((1, 1), 32768, 32, 128, 0.0, 0.0, 1.0, 0.999, MISTRAL_7B, id="1x1-Mistral7B-v32768-b32-s128-no-pen"),
pytest.param((1, 1), 32768, 32, 128, 1.2, 1.2, 1.5, 0.95, MISTRAL_7B, id="1x1-Mistral7B-v32768-b32-s128-pen", marks=_slow),
pytest.param((1, 1), 32768, 32, 1024, 0.0, 0.0, 1.0, 0.999, MISTRAL_7B, id="1x1-Mistral7B-v32768-b32-s1024-no-pen", marks=_slow),
pytest.param((1, 1), 32768, 32, 1024, 1.2, 1.2, 1.5, 0.95, MISTRAL_7B, id="1x1-Mistral7B-v32768-b32-s1024-pen", marks=_slow),
pytest.param((1, 1), 32768, 32, 2048, 0.0, 0.0, 1.0, 0.999, MISTRAL_7B, id="1x1-Mistral7B-v32768-b32-s2048-no-pen", marks=_slow),
pytest.param((1, 1), 32768, 32, 2048, 1.2, 1.2, 1.5, 0.95, MISTRAL_7B, id="1x1-Mistral7B-v32768-b32-s2048-pen", marks=_slow),
pytest.param((1, 1), 32768, 32, 4096, 0.0, 0.0, 1.0, 0.999, MISTRAL_7B, id="1x1-Mistral7B-v32768-b32-s4096-no-pen", marks=_slow),
pytest.param((1, 1), 32768, 32, 4096, 1.2, 1.2, 1.5, 0.95, MISTRAL_7B, id="1x1-Mistral7B-v32768-b32-s4096-pen", marks=_slow),
# --- (1,1) Llama8B v128256 ---
pytest.param((1, 1), 128256, 1, 125, 0.0, 0.0, 1.0, 0.999, LLAMA_8B, id="1x1-Llama8B-v128256-b1-s125-no-pen"),
# --- (1,1) Llama1B v128256 ---
pytest.param((1, 1), 128256, 1, 71, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b1-s71-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 1, 80, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b1-s80-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 1, 115, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b1-s115-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 1, 337, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b1-s337-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 1, 512, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b1-s512-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 1, 785, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b1-s785-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 1, 16229, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b1-s16229-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 52, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s52-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 56, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s56-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 57, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s57-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 59, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s59-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 60, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s60-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 61, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s61-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 63, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s63-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 65, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s65-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 66, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s66-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 67, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s67-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 70, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s70-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 71, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s71-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 72, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s72-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 75, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s75-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 77, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s77-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 80, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s80-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 81, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s81-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 84, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s84-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 86, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s86-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 87, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s87-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 91, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s91-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 93, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s93-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 94, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s94-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 96, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s96-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 98, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s98-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 99, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s99-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 101, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s101-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 102, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s102-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 103, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s103-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 104, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s104-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 105, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s105-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 106, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s106-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 109, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s109-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 113, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s113-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 114, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s114-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 115, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s115-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 116, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s116-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 117, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s117-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 119, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s119-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 124, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s124-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 125, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s125-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 128, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s128-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 128, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s128-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 337, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s337-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 512, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s512-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 712, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s712-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 785, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s785-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 1024, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s1024-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 1024, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s1024-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 2048, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s2048-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 2048, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s2048-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 4096, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s4096-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 4096, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s4096-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 8192, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s8192-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 8192, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s8192-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 16229, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s16229-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 16384, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s16384-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 16384, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s16384-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 32768, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s32768-no-pen", marks=_slow),
pytest.param((1, 1), 128256, 32, 32768, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x1-Llama1B-v128256-b32-s32768-pen", marks=_slow),
# --- (1,2) Mistral7B v32768 ---
pytest.param((1, 2), 32768, 32, 128, 0.0, 0.0, 1.0, 0.999, MISTRAL_7B, id="1x2-Mistral7B-v32768-b32-s128-no-pen"),
pytest.param((1, 2), 32768, 32, 128, 1.2, 1.2, 1.5, 0.95, MISTRAL_7B, id="1x2-Mistral7B-v32768-b32-s128-pen", marks=_slow),
pytest.param((1, 2), 32768, 32, 1024, 0.0, 0.0, 1.0, 0.999, MISTRAL_7B, id="1x2-Mistral7B-v32768-b32-s1024-no-pen", marks=_slow),
pytest.param((1, 2), 32768, 32, 1024, 1.2, 1.2, 1.5, 0.95, MISTRAL_7B, id="1x2-Mistral7B-v32768-b32-s1024-pen", marks=_slow),
pytest.param((1, 2), 32768, 32, 2048, 0.0, 0.0, 1.0, 0.999, MISTRAL_7B, id="1x2-Mistral7B-v32768-b32-s2048-no-pen", marks=_slow),
pytest.param((1, 2), 32768, 32, 2048, 1.2, 1.2, 1.5, 0.95, MISTRAL_7B, id="1x2-Mistral7B-v32768-b32-s2048-pen", marks=_slow),
pytest.param((1, 2), 32768, 32, 4096, 0.0, 0.0, 1.0, 0.999, MISTRAL_7B, id="1x2-Mistral7B-v32768-b32-s4096-no-pen", marks=_slow),
pytest.param((1, 2), 32768, 32, 4096, 1.2, 1.2, 1.5, 0.95, MISTRAL_7B, id="1x2-Mistral7B-v32768-b32-s4096-pen", marks=_slow),
# --- (1,2) Llama1B v128256 ---
pytest.param((1, 2), 128256, 1, 71, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b1-s71-no-pen"),
pytest.param((1, 2), 128256, 1, 80, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b1-s80-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 1, 115, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b1-s115-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 1, 337, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b1-s337-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 1, 512, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b1-s512-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 1, 785, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b1-s785-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 1, 16229, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b1-s16229-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 52, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s52-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 56, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s56-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 57, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s57-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 59, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s59-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 60, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s60-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 61, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s61-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 63, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s63-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 65, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s65-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 66, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s66-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 67, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s67-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 70, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s70-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 71, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s71-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 72, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s72-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 75, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s75-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 77, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s77-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 80, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s80-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 81, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s81-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 84, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s84-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 86, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s86-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 87, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s87-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 91, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s91-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 93, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s93-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 94, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s94-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 96, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s96-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 98, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s98-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 99, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s99-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 101, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s101-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 102, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s102-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 103, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s103-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 104, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s104-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 105, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s105-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 106, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s106-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 109, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s109-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 113, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s113-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 114, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s114-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 115, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s115-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 116, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s116-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 117, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s117-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 119, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s119-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 124, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s124-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 125, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s125-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 128, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s128-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 128, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s128-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 337, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s337-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 512, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s512-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 712, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s712-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 785, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s785-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 1024, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s1024-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 1024, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s1024-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 2048, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s2048-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 2048, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s2048-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 4096, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s4096-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 4096, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s4096-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 8192, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s8192-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 8192, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s8192-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 16229, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s16229-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 16384, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s16384-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 16384, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s16384-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 32768, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s32768-no-pen", marks=_slow),
pytest.param((1, 2), 128256, 32, 32768, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x2-Llama1B-v128256-b32-s32768-pen", marks=_slow),
# --- (1,8) Mixtral8x7B v32000 ---
pytest.param((1, 8), 32000, 1, 87, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b1-s87-no-pen"),
pytest.param((1, 8), 32000, 1, 393, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b1-s393-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 57, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s57-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 61, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s61-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 64, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s64-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 66, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s66-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 67, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s67-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 69, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s69-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 70, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s70-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 72, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s72-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 73, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s73-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 74, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s74-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 76, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s76-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 79, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s79-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 82, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s82-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 83, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s83-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 87, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s87-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 89, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s89-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 101, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s101-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 128, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s128-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 128, 1.2, 1.2, 1.5, 0.95, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s128-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 393, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s393-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 820, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s820-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 1024, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s1024-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 1024, 1.2, 1.2, 1.5, 0.95, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s1024-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 2048, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s2048-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 2048, 1.2, 1.2, 1.5, 0.95, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s2048-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 4096, 0.0, 0.0, 1.0, 0.999, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s4096-no-pen", marks=_slow),
pytest.param((1, 8), 32000, 32, 4096, 1.2, 1.2, 1.5, 0.95, MIXTRAL_8X7B, id="1x8-Mixtral8x7B-v32000-b32-s4096-pen", marks=_slow),
# --- (1,8) Mistral7B v32768 ---
pytest.param((1, 8), 32768, 32, 128, 0.0, 0.0, 1.0, 0.999, MISTRAL_7B, id="1x8-Mistral7B-v32768-b32-s128-no-pen"),
pytest.param((1, 8), 32768, 32, 128, 1.2, 1.2, 1.5, 0.95, MISTRAL_7B, id="1x8-Mistral7B-v32768-b32-s128-pen", marks=_slow),
pytest.param((1, 8), 32768, 32, 1024, 0.0, 0.0, 1.0, 0.999, MISTRAL_7B, id="1x8-Mistral7B-v32768-b32-s1024-no-pen", marks=_slow),
pytest.param((1, 8), 32768, 32, 1024, 1.2, 1.2, 1.5, 0.95, MISTRAL_7B, id="1x8-Mistral7B-v32768-b32-s1024-pen", marks=_slow),
pytest.param((1, 8), 32768, 32, 2048, 0.0, 0.0, 1.0, 0.999, MISTRAL_7B, id="1x8-Mistral7B-v32768-b32-s2048-no-pen", marks=_slow),
pytest.param((1, 8), 32768, 32, 2048, 1.2, 1.2, 1.5, 0.95, MISTRAL_7B, id="1x8-Mistral7B-v32768-b32-s2048-pen", marks=_slow),
pytest.param((1, 8), 32768, 32, 4096, 0.0, 0.0, 1.0, 0.999, MISTRAL_7B, id="1x8-Mistral7B-v32768-b32-s4096-no-pen", marks=_slow),
pytest.param((1, 8), 32768, 32, 4096, 1.2, 1.2, 1.5, 0.95, MISTRAL_7B, id="1x8-Mistral7B-v32768-b32-s4096-pen", marks=_slow),
# --- (1,8) Llama1B v128256 ---
pytest.param((1, 8), 128256, 1, 71, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b1-s71-no-pen"),
pytest.param((1, 8), 128256, 1, 80, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b1-s80-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 1, 115, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b1-s115-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 1, 337, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b1-s337-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 1, 512, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b1-s512-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 1, 785, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b1-s785-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 1, 16229, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b1-s16229-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 52, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s52-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 56, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s56-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 57, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s57-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 59, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s59-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 60, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s60-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 61, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s61-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 63, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s63-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 65, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s65-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 66, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s66-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 67, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s67-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 70, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s70-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 71, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s71-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 72, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s72-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 75, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s75-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 77, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s77-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 80, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s80-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 81, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s81-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 84, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s84-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 86, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s86-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 87, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s87-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 91, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s91-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 93, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s93-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 94, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s94-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 96, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s96-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 98, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s98-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 99, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s99-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 101, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s101-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 102, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s102-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 103, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s103-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 104, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s104-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 105, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s105-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 106, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s106-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 109, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s109-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 113, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s113-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 114, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s114-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 115, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s115-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 116, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s116-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 117, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s117-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 119, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s119-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 124, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s124-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 125, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s125-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 128, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s128-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 128, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s128-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 337, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s337-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 512, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s512-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 712, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s712-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 785, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s785-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 1024, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s1024-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 1024, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s1024-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 2048, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s2048-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 2048, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s2048-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 4096, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s4096-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 4096, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s4096-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 8192, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s8192-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 8192, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s8192-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 16229, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s16229-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 16384, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s16384-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 16384, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s16384-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 32768, 0.0, 0.0, 1.0, 0.999, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s32768-no-pen", marks=_slow),
pytest.param((1, 8), 128256, 32, 32768, 1.2, 1.2, 1.5, 0.95, LLAMA_1B, id="1x8-Llama1B-v128256-b32-s32768-pen", marks=_slow),
# --- (1,8) Qwen3-32B v151936 ---
pytest.param((1, 8), 151936, 32, 128, 1.2, 1.2, 1.5, 0.95, QWEN3_32B, id="1x8-Qwen3-32B-v151936-b32-s128-pen"),
# --- (1,8) Qwen2.5-72B v152064 ---
pytest.param((1, 8), 152064, 1, 65, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b1-s65-no-pen"),
pytest.param((1, 8), 152064, 1, 80, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b1-s80-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 1, 109, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b1-s109-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 1, 421, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b1-s421-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 1, 512, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b1-s512-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 1, 780, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b1-s780-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 48, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s48-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 50, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s50-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 51, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s51-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 54, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s54-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 55, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s55-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 56, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s56-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 57, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s57-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 59, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s59-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 60, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s60-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 61, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s61-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 64, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s64-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 65, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s65-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 68, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s68-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 69, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s69-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 71, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s71-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 74, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s74-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 75, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s75-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 76, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s76-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 80, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s80-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 81, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s81-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 85, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s85-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 87, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s87-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 88, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s88-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 90, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s90-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 92, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s92-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 93, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s93-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 95, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s95-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 96, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s96-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 97, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s97-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 98, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s98-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 99, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s99-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 100, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s100-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 101, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s101-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 102, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s102-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 103, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s103-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 107, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s107-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 108, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s108-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 109, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s109-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 110, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s110-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 111, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s111-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 113, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s113-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 118, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s118-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 119, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s119-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 128, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s128-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 128, 1.2, 1.2, 1.5, 0.95, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s128-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 421, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s421-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 512, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s512-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 707, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s707-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 780, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s780-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 1024, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s1024-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 1024, 1.2, 1.2, 1.5, 0.95, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s1024-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 2048, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s2048-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 2048, 1.2, 1.2, 1.5, 0.95, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s2048-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 4096, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s4096-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 4096, 1.2, 1.2, 1.5, 0.95, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s4096-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 8192, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s8192-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 8192, 1.2, 1.2, 1.5, 0.95, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s8192-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 16255, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s16255-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 16384, 0.0, 0.0, 1.0, 0.999, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s16384-no-pen", marks=_slow),
pytest.param((1, 8), 152064, 32, 16384, 1.2, 1.2, 1.5, 0.95, QWEN25_72B, id="1x8-Qwen2.5-72B-v152064-b32-s16384-pen", marks=_slow),
]
# fmt: on
# ==============================================================================
# Reference implementation (pure torch)
# ==============================================================================
def reference_apply_penalties(logits, prompt_mask, output_mask, output_counts, presence, frequency, repetition):
"""Pure-torch reference for penalty math, following the OpenAI API spec.
Algorithm source: vLLM's ``apply_penalties`` in ``vllm/model_executor/layers/utils.py``
(https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/utils.py).
- Presence: subtract flat penalty for each token that appeared in output
- Frequency: subtract penalty proportional to token occurrence count
- Repetition: sign-dependent scaling for tokens in prompt OR output
(positive logits divided by penalty, negative logits multiplied by penalty)
"""
logits = logits.clone().float()
output_mask_f = output_mask.float()
output_counts_f = output_counts.float()
# Presence: logits -= output_mask * presence (vLLM: presence_penalties * output_mask)
logits -= output_mask_f * presence
# Frequency: logits -= output_counts * frequency (vLLM: frequency_penalties * output_bin_counts)
logits -= output_counts_f * frequency
# Repetition: sign-dependent scaling (vLLM: apply_repetition_penalties on combined prompt+output mask)
combined = ((prompt_mask + output_mask) > 0).float()
inv_rep = 1.0 / repetition
# If logit > 0: multiply by 1/rep (shrink toward 0). If logit <= 0: multiply by rep (push away from 0).
scale = torch.where(
logits > 0,
torch.where(combined.bool(), inv_rep, torch.ones_like(logits)),
torch.where(combined.bool(), repetition, torch.ones_like(logits)),
)
logits *= scale
return logits
# ==============================================================================
# Unit tests: Config and dataclasses (no device)
# ==============================================================================
class TestConfigUnit:
def test_config_defaults(self):
cfg = Penalties1DConfig(vocab_size=1024)
assert cfg.max_batch_size == 32
assert cfg.mesh_device is None
assert cfg.sub_core_grids is None
assert cfg.prompt_mask is None
def test_config_not_resolved_without_mesh_device(self):
cfg = Penalties1DConfig(vocab_size=1024)
assert not cfg.is_resolved()
def test_penalty_params_fields(self):
fields = PenaltyParams.__dataclass_fields__
assert set(fields.keys()) == {
"prompt_mask",
"presence_penalties",
"frequency_penalties",
"repetition_penalties",
"inverse_repetition_penalties",
}
def test_penalty_accumulator_fields(self):
fields = PenaltyAccumulator.__dataclass_fields__
assert set(fields.keys()) == {"output_mask", "output_counts", "output_counts_gathered"}
# ==============================================================================
# Device tests: Config resolution and Penalties1D
# ==============================================================================
@pytest.mark.parametrize("ttnn_mesh_device", [(1, 1), (1, 2), (1, 8)], ids=["1x1", "1x2", "1x8"], indirect=True)
class TestPenalties1DDevice:
@pytest.mark.parametrize("vocab_size", [1024])
def test_resolve_config(self, ttnn_mesh_device, vocab_size):
cfg = Penalties1DConfig(vocab_size=vocab_size, mesh_device=ttnn_mesh_device)
resolved = _resolve_penalties1d_config(cfg)
assert resolved.is_resolved()
assert resolved.mesh_device is ttnn_mesh_device
@pytest.mark.parametrize("vocab_size", [1024])
def test_load_device_buffers(self, ttnn_mesh_device, vocab_size):
pen = Penalties1D(vocab_size=vocab_size, mesh_device=ttnn_mesh_device)
pen.load_device_buffers()
assert pen._device_buffers_loaded
assert isinstance(pen._decode_src, ttnn.Tensor)
assert isinstance(pen._zeros, ttnn.Tensor)
@pytest.mark.parametrize("vocab_size", [1024])
def test_decode_forward_none_passthrough(self, ttnn_mesh_device, vocab_size):
"""forward() with None params/accum returns logits unchanged."""
pen = Penalties1D(vocab_size=vocab_size, mesh_device=ttnn_mesh_device)
logits_host = torch.randn(32, vocab_size, dtype=torch.bfloat16)
logits_tt = ttnn.from_torch(logits_host, device=ttnn_mesh_device, dtype=ttnn.bfloat16, layout=ttnn.TILE_LAYOUT)
result = pen.forward(logits_tt, params=None, accum=None)
assert result is logits_tt
@pytest.mark.parametrize("vocab_size", [1024])
def test_from_model_args(self, ttnn_mesh_device, vocab_size):
"""from_model_args backward compat factory."""
class MockArgs:
padded_vocab_size = vocab_size
sub_core_grids = None
pen = Penalties1D.from_model_args(ttnn_mesh_device, MockArgs())
assert pen.config.vocab_size == vocab_size
assert pen.config.mesh_device is ttnn_mesh_device
def test_rejects_galaxy(self, ttnn_mesh_device):
"""from_model_args should reject 2D (Galaxy) topologies."""
class FakeMesh:
shape = (2, 4)
class MockArgs:
padded_vocab_size = 1024
sub_core_grids = None
with pytest.raises(ValueError, match="1D mesh topologies"): # allow-pytest.raises: pre-existing
Penalties1D.from_model_args(FakeMesh(), MockArgs())
# ==============================================================================
# VS Reference tests — full penalty pipeline compared against pure-torch golden
# ==============================================================================
def _make_penalty_tensors_on_device(
ttnn_mesh_device,
B,
*,
prompt_mask_host,
output_mask_host,
output_counts_host,
presence_val,
frequency_val,
repetition_val,
):
"""Helper: build PenaltyParams + PenaltyAccumulator on device from host tensors."""
params = PenaltyParams(
prompt_mask=ttnn.from_torch(
prompt_mask_host,
device=ttnn_mesh_device,
dtype=ttnn.int32,
layout=ttnn.TILE_LAYOUT,
memory_config=ttnn.DRAM_MEMORY_CONFIG,
),
presence_penalties=ttnn.from_torch(
torch.full((B, 1), presence_val),
device=ttnn_mesh_device,
dtype=ttnn.bfloat16,
layout=ttnn.TILE_LAYOUT,
),
frequency_penalties=ttnn.from_torch(
torch.full((B, 1), frequency_val),
device=ttnn_mesh_device,
dtype=ttnn.bfloat16,
layout=ttnn.TILE_LAYOUT,
),
repetition_penalties=ttnn.from_torch(
torch.full((B, 1), repetition_val),
device=ttnn_mesh_device,
dtype=ttnn.bfloat16,
layout=ttnn.TILE_LAYOUT,
),
inverse_repetition_penalties=ttnn.from_torch(
torch.full((B, 1), 1.0 / repetition_val),
device=ttnn_mesh_device,
dtype=ttnn.bfloat16,
layout=ttnn.TILE_LAYOUT,
),
)
accum = PenaltyAccumulator(
output_mask=ttnn.from_torch(
output_mask_host,
device=ttnn_mesh_device,
dtype=ttnn.int32,
layout=ttnn.TILE_LAYOUT,
memory_config=ttnn.DRAM_MEMORY_CONFIG,
),
output_counts=ttnn.from_torch(
output_counts_host,
device=ttnn_mesh_device,
dtype=ttnn.int32,
layout=ttnn.TILE_LAYOUT,
memory_config=ttnn.DRAM_MEMORY_CONFIG,
),
output_counts_gathered=ttnn.from_torch(
output_counts_host,
device=ttnn_mesh_device,
dtype=ttnn.int32,
layout=ttnn.TILE_LAYOUT,
memory_config=ttnn.DRAM_MEMORY_CONFIG,
),
)
return params, accum
def _readback_logits(result_tt, ttnn_mesh_device, B, vocab_size):
"""Helper: read logits back from device to host torch tensor."""
result_host = ttnn.to_torch(
result_tt,
mesh_composer=ttnn.ConcatMesh2dToTensor(
ttnn_mesh_device, dims=(0, 1), mesh_shape=tuple(ttnn_mesh_device.shape)
),
)
return result_host[:B, :vocab_size].float()
@pytest.mark.parametrize("ttnn_mesh_device", [(1, 1), (1, 2), (1, 8)], ids=["1x1", "1x2", "1x8"], indirect=True)
@pytest.mark.parametrize(
"mesh_shape,vocab_size,batch_size,seq_len,presence,frequency,repetition,pcc,hf_model_name",
_list_collected_penalty_cases(),
)
def test_penalties1d_vs_reference(
ttnn_mesh_device,
mesh_shape,
vocab_size,
batch_size,
seq_len,
presence,
frequency,
repetition,
pcc,
hf_model_name,
):
"""
Test Penalties1D.decode_forward matches the pure-torch reference_apply_penalties.
Parametrized across penalty combinations to verify each penalty type independently
and in combination. PCC thresholds account for bfloat16 precision.
"""
torch.manual_seed(42)
B = 32
pen = Penalties1D(vocab_size=vocab_size, mesh_device=ttnn_mesh_device)
# Build host tensors with realistic patterns
logits_host = torch.randn(B, vocab_size, dtype=torch.bfloat16)
# prompt_mask: first 50 tokens were in the prompt
prompt_mask_host = torch.zeros(B, vocab_size, dtype=torch.int32)
prompt_mask_host[:, :50] = 1
# output_mask: tokens 40-60 appeared in output (overlaps with prompt)
output_mask_host = torch.zeros(B, vocab_size, dtype=torch.int32)
output_mask_host[:, 40:60] = 1
# output_counts: tokens 40-60 appeared 1-3 times
output_counts_host = torch.zeros(B, vocab_size, dtype=torch.int32)
output_counts_host[:, 40:50] = 2
output_counts_host[:, 50:60] = 1
# --- Reference (pure torch) ---
expected = reference_apply_penalties(
logits_host,
prompt_mask_host,
output_mask_host,
output_counts_host,
presence,
frequency,
repetition,
)
# --- TT device ---
logits_tt = ttnn.from_torch(
logits_host,
device=ttnn_mesh_device,
dtype=ttnn.bfloat16,
layout=ttnn.TILE_LAYOUT,
)
params, accum = _make_penalty_tensors_on_device(
ttnn_mesh_device,
B,
prompt_mask_host=prompt_mask_host,
output_mask_host=output_mask_host,
output_counts_host=output_counts_host,
presence_val=presence,
frequency_val=frequency,
repetition_val=repetition,
)
result_tt = pen.decode_forward(logits_tt, params, accum)
result_host = _readback_logits(result_tt, ttnn_mesh_device, B, vocab_size)
passing, pcc_msg = comp_pcc(expected, result_host, pcc=pcc)
assert passing, f"Penalties1D vs reference failed: {pcc_msg} (threshold={pcc})"
@pytest.mark.parametrize("ttnn_mesh_device", [(1, 1), (1, 2), (1, 8)], ids=["1x1", "1x2", "1x8"], indirect=True)
def test_penalties1d_changes_argmax(ttnn_mesh_device):
"""
Heavy repetition penalty should change which token has the highest logit.
Setup: token 0 has the highest logit AND appears in prompt+output.
With repetition=5.0, the penalty should push token 0's logit down far enough
that a different token becomes the argmax.
"""
torch.manual_seed(123)
B = 32
vocab_size = 1024
pen = Penalties1D(vocab_size=vocab_size, mesh_device=ttnn_mesh_device)
# Token 0 is the clear winner in raw logits
logits_host = torch.randn(B, vocab_size, dtype=torch.bfloat16)
logits_host[:, 0] = 10.0 # make token 0 dominant
# Token 0 appears in prompt and output
prompt_mask_host = torch.zeros(B, vocab_size, dtype=torch.int32)
prompt_mask_host[:, 0] = 1
output_mask_host = torch.zeros(B, vocab_size, dtype=torch.int32)
output_mask_host[:, 0] = 1
output_counts_host = torch.zeros(B, vocab_size, dtype=torch.int32)
output_counts_host[:, 0] = 3
# Original argmax should be token 0
assert logits_host[0].argmax().item() == 0
logits_tt = ttnn.from_torch(
logits_host,
device=ttnn_mesh_device,
dtype=ttnn.bfloat16,
layout=ttnn.TILE_LAYOUT,
)
params, accum = _make_penalty_tensors_on_device(
ttnn_mesh_device,
B,
prompt_mask_host=prompt_mask_host,
output_mask_host=output_mask_host,
output_counts_host=output_counts_host,
presence_val=2.0,
frequency_val=2.0,
repetition_val=5.0,
)
result_tt = pen.decode_forward(logits_tt, params, accum)
result_host = _readback_logits(result_tt, ttnn_mesh_device, B, vocab_size)
# After heavy penalties, token 0 should no longer be argmax
new_argmax = result_host[0].argmax().item()
assert new_argmax != 0, f"Expected penalty to change argmax from 0, but it's still {new_argmax}"
# ==============================================================================
# Helper: build topology-correct PenaltyParams + PenaltyAccumulator from config
# ==============================================================================
def _make_proper_params_accum(pen: Penalties1D):
"""Build PenaltyParams + PenaltyAccumulator from the module's resolved config.
Uses the config's LazyBuffer mesh_mappers to guarantee the correct dtype/layout/
sharding for whatever device topology is active. This is required for methods like
init_prompt_penalties and update_output_tokens that call _token_bin_counts_and_mask,
which expects properly sharded output tensors.
"""
params = PenaltyParams(
prompt_mask=_materialize(pen.config.prompt_mask),
presence_penalties=_materialize(pen.config.presence_penalties),
frequency_penalties=_materialize(pen.config.frequency_penalties),
repetition_penalties=_materialize(pen.config.repetition_penalties),
inverse_repetition_penalties=_materialize(pen.config.inverse_repetition_penalties),
)
accum = PenaltyAccumulator(
output_mask=_materialize(pen.config.output_mask),
output_counts=_materialize(pen.config.output_counts),
output_counts_gathered=_materialize(pen.config.output_counts_gathered),
)
return params, accum
# ==============================================================================
# Additional unit tests (no device)
# ==============================================================================
class TestConfigUnitMore:
def test_buf_resolved_with_lazy_buffer(self):
"""_buf_resolved calls buf.is_resolved() for a LazyBuffer (line 97)."""
from models.common.modules.lazy_buffer import LazyBuffer
lb = LazyBuffer(source=torch.zeros(1), device=None)
assert not Penalties1DConfig._buf_resolved(lb) # is_resolved() → False (device=None)
def test_buf_resolved_returns_false_for_none(self):
"""_buf_resolved returns False for None (baseline, complements line 95/97 tests)."""
assert not Penalties1DConfig._buf_resolved(None)
# ==============================================================================
# release() / cleanup contract (no device)
# ==============================================================================
# Module-owned buffer fields, in the order Penalties1D.release() walks them.
_OWNED_BUFFER_NAMES = (
"prompt_mask",
"output_mask",
"output_counts",
"output_counts_gathered",
"zeros",
"decode_src",
"presence_penalties",
"frequency_penalties",
"repetition_penalties",
"inverse_repetition_penalties",
)
class _NotingError(RuntimeError):
"""RuntimeError with an add_note() on every interpreter, so the note branch of the
cleanup helpers is exercised on Python 3.10 (CI) as well as 3.11+."""
def __init__(self, message):
super().__init__(message)
self.notes = ()
def add_note(self, note):
self.notes = self.notes + (note,)
def _loaded_penalties_with_fake_buffers():
"""A Penalties1D that looks loaded: ten owned LazyBuffers and two slice tensors hold
fake device handles. Returns (penalties, buffers, handles-in-release-order)."""
buffers = {}
values = []
for name in _OWNED_BUFFER_NAMES:
buffer = LazyBuffer(source=torch.zeros(1))
value = object()
buffer._value = value
buffers[name] = buffer
values.append(value)
penalties = object.__new__(Penalties1D)
penalties.config = SimpleNamespace(**buffers)
penalties._slice_start = object()
penalties._slice_end = object()
values.extend((penalties._slice_start, penalties._slice_end))
penalties._decode_src = buffers["decode_src"]._value
penalties._zeros = buffers["zeros"]._value
penalties._device_buffers_loaded = True
return penalties, buffers, values
def test_penalties_release_deallocates_owned_lazy_buffers_and_slice_tensors(monkeypatch):
released = []
monkeypatch.setattr(penalties_module.ttnn, "deallocate", released.append)
penalties, buffers, values = _loaded_penalties_with_fake_buffers()
penalties.release()
penalties.release() # idempotent: nothing left to deallocate
assert released == values
assert all(buffer._value is None for buffer in buffers.values())
assert penalties._slice_start is None and penalties._slice_end is None
assert penalties._decode_src is None and penalties._zeros is None
assert not penalties._device_buffers_loaded
def test_penalties_release_is_best_effort_and_retries_only_failed_buffers(monkeypatch, expect_error):
penalties, buffers, values = _loaded_penalties_with_fake_buffers()
counts_handle = buffers["output_counts"]._value
slice_handle = penalties._slice_start
counts_error = _NotingError("output_counts deallocate failed once")
slice_error = RuntimeError("slice_start deallocate failed once")
failures = {counts_handle: counts_error, slice_handle: slice_error}
attempts = []
def deallocate(value):
attempts.append(value)
if value in failures and attempts.count(value) == 1:
raise failures[value]
monkeypatch.setattr(penalties_module.ttnn, "deallocate", deallocate)
with expect_error(RuntimeError, "output_counts deallocate failed once") as caught:
penalties.release()
# First failure is raised; later failures ride along on it.
assert caught.value is counts_error
assert caught.value.cleanup_failures == (slice_error,)
assert counts_error.notes == ("cleanup also encountered 1 additional failure(s)",)
# Every buffer was attempted once; only the failed ones keep their handles.
assert attempts == values
assert buffers["output_counts"]._value is not None
assert all(buffer._value is None for name, buffer in buffers.items() if name != "output_counts")
assert penalties._slice_start is not None
assert penalties._slice_end is None
assert penalties._decode_src is None and penalties._zeros is None
assert not penalties._device_buffers_loaded
penalties.release()
# Retry touches only the two buffers that failed, and now clears them.
assert attempts == values + [counts_handle, slice_handle]
assert all(buffer._value is None for buffer in buffers.values())
assert penalties._slice_start is None
def test_load_device_buffers_failure_releases_partial_state_and_attaches_cleanup_failures(monkeypatch, expect_error):
decode_src = LazyBuffer(source=torch.zeros(1))
decode_src._value = object()
zeros = LazyBuffer(source=torch.zeros(1))
zeros._value = object()
penalties = object.__new__(Penalties1D)
penalties.config = SimpleNamespace(
decode_src=decode_src,
zeros=zeros,
mesh_device=SimpleNamespace(shape=(1, 8)),
sub_core_grids=None,
is_resolved=lambda: True,
)
penalties._device_buffers_loaded = False
allocation_error = _NotingError("slice tensors failed")
cleanup_error = RuntimeError("zeros deallocate failed once")
deallocated = []
def deallocate(value):
deallocated.append(value)
if value is zeros._value and deallocated.count(value) == 1:
raise cleanup_error
def build_slice_tensors_and_fail():
raise allocation_error
monkeypatch.setattr(penalties_module.ttnn, "ShardTensor2dMesh", lambda *args, **kwargs: object())
monkeypatch.setattr(penalties_module.ttnn, "deallocate", deallocate)
monkeypatch.setattr(penalties, "_build_slice_tensors", build_slice_tensors_and_fail)
with expect_error(RuntimeError, "slice tensors failed") as caught:
penalties.load_device_buffers()
# The allocation failure is what propagates; the cleanup failure is attached, not raised.
assert caught.value is allocation_error
assert caught.value.cleanup_failures == (cleanup_error,)
assert allocation_error.notes == ("cleanup also encountered 1 failure(s)",)
assert not penalties._device_buffers_loaded
assert decode_src._value is None # released during cleanup
assert zeros._value is not None # deallocate failed once, handle retained for retry
penalties.release()
assert zeros._value is None
assert deallocated.count(decode_src._value) == 0 and len(deallocated) == 3
# A later load succeeds and repopulates the module-owned state.
slices = (object(), object())
monkeypatch.setattr(penalties, "_build_slice_tensors", lambda: slices)
monkeypatch.setattr(penalties_module, "_materialize", lambda buf: object())
penalties.load_device_buffers()
assert penalties._device_buffers_loaded
assert (penalties._slice_start, penalties._slice_end) == slices
assert penalties._cluster_shape == (1, 8) and penalties._num_devices == 8
def test_cleanup_failure_helpers_tolerate_exceptions_without_add_note(expect_error):
"""Plain exceptions (no add_note on Python 3.10) still carry cleanup_failures."""
primary = RuntimeError("primary")
penalties_module._attach_cleanup_failures(primary, ())
assert not hasattr(primary, "cleanup_failures")
penalties_module._attach_cleanup_failures(primary, (ValueError("first"),))
penalties_module._attach_cleanup_failures(primary, (ValueError("second"),))
assert [str(error) for error in primary.cleanup_failures] == ["first", "second"]
lone = RuntimeError("lone failure")
with expect_error(RuntimeError, "lone failure") as caught:
penalties_module._raise_cleanup_failures([lone])
assert caught.value is lone
assert not hasattr(lone, "cleanup_failures")
head, tail = RuntimeError("head failure"), ValueError("tail failure")
with expect_error(RuntimeError, "head failure") as caught:
penalties_module._raise_cleanup_failures([head, tail])
assert caught.value is head
assert head.cleanup_failures == (tail,)
def test_build_slice_tensors_releases_first_slice_when_second_allocation_fails(monkeypatch, expect_error):
penalties = object.__new__(Penalties1D)
penalties.config = SimpleNamespace(vocab_size=1024, max_batch_size=32, mesh_device=object())
penalties._cluster_shape = (1, 8)
penalties._num_devices = 8
first_slice = object()
allocation_error = _NotingError("slice_end allocation failed")
cleanup_error = RuntimeError("slice_start deallocate failed")
host_tensors = []
deallocated = []
def from_torch(tensor, **_kwargs):
host_tensors.append(tensor)
if len(host_tensors) == 1:
return first_slice
raise allocation_error
def deallocate(value):
deallocated.append(value)
raise cleanup_error
monkeypatch.setattr(penalties_module.ttnn, "ShardTensor2dMesh", lambda *args, **kwargs: object())
monkeypatch.setattr(penalties_module.ttnn, "from_torch", from_torch)
monkeypatch.setattr(penalties_module.ttnn, "deallocate", deallocate)
with expect_error(RuntimeError, "slice_end allocation failed") as caught:
penalties._build_slice_tensors()
assert caught.value is allocation_error
assert deallocated == [first_slice] # the half-built pair is torn down before re-raising
assert caught.value.cleanup_failures == (cleanup_error,)
assert allocation_error.notes == ("cleanup also encountered 1 failure(s)",)
# Per-device vocab slice bounds for 1024 / 8 devices, padded batch 32.
assert host_tensors[0].tolist() == [v for d in range(8) for v in (0, 128 * d)]
assert host_tensors[1].tolist() == [v for d in range(8) for v in (32, 128 * (d + 1))]
# ==============================================================================
# Additional device tests: coverage for previously untested methods
# ==============================================================================
@pytest.mark.parametrize(
("batch_height", "expected_operation"),
[(1, "to_layout"), (32, "tilize")],
)
def test_histogram_tilize_preserves_batch32_path_and_pads_smaller_batches(
monkeypatch,
batch_height,
expected_operation,
):
"""Only non-tile batch heights use the padding-aware layout conversion."""
calls = []
counts = SimpleNamespace(padded_shape=(batch_height, 1024))
result = object()
pen = object.__new__(Penalties1D)
pen._op_kwargs = {"sub_core_grids": "sub-grid"}
pen._use_low_perf_tilize = True
monkeypatch.setattr(
ttnn,
"tilize",
lambda tensor, **kwargs: calls.append(("tilize", tensor, kwargs)) or result,
)
monkeypatch.setattr(
ttnn,
"to_layout",
lambda tensor, layout, **kwargs: calls.append(("to_layout", tensor, layout, kwargs)) or result,
)
assert pen._tilize_counts(counts) is result
if expected_operation == "tilize":
assert calls == [
(
"tilize",
counts,
{"sub_core_grids": "sub-grid", "use_low_perf": True},
)
]
else:
assert calls == [
(
"to_layout",
counts,
ttnn.TILE_LAYOUT,
{"sub_core_grids": "sub-grid"},
)
]
def test_non_tile_histogram_slices_padded_views_and_preserves_logical_output(monkeypatch):
"""Batch-1 vocab slicing uses padded aliases of caller-owned state."""
events = []
counts_new_rm = SimpleNamespace(name="counts-new-rm")
counts_new_tiled = SimpleNamespace(name="counts-new-tiled")
counts = SimpleNamespace(name="counts", shape=(1, 1024), padded_shape=(32, 1024))
counts_padded = SimpleNamespace(name="counts-padded")
counts_sliced = SimpleNamespace(name="counts-sliced", shape=(1, 128), padded_shape=(32, 128))
counts_sliced_padded = SimpleNamespace(name="counts-sliced-padded")
mask = object()
new_tokens = SimpleNamespace(deallocate=lambda: events.append("deallocate-tokens"))
pen = object.__new__(Penalties1D)
pen.config = SimpleNamespace(max_batch_size=1)
pen._zeros = object()
pen._op_kwargs = {}
pen._slice_start = object()
pen._slice_end = object()
pen._num_devices = 8
pen._tilize_counts = lambda tensor: events.append(("tilize", tensor)) or counts_new_tiled
monkeypatch.setattr(
ttnn,
"scatter_add",
lambda *args, **kwargs: events.append(("scatter", args, kwargs)) or counts_new_rm,
)
monkeypatch.setattr(
ttnn,
"add",
lambda lhs, rhs, *, output_tensor, **kwargs: events.append(("add", lhs, rhs, output_tensor, kwargs))
or output_tensor,
)
def reshape(tensor, logical_shape, padded_shape, *, skip_padding_fill):
events.append(("reshape", tensor, logical_shape, padded_shape, skip_padding_fill))
if tensor is counts:
return counts_padded
assert tensor is counts_sliced
return counts_sliced_padded
monkeypatch.setattr(ttnn, "reshape", reshape)
def slice_tensor(tensor, start, end, *, output_tensor, slice_dim, num_devices, **kwargs):
events.append(("slice", tensor, start, end, output_tensor, slice_dim, num_devices, kwargs))
assert tensor is counts_padded
assert end is pen._slice_end
assert output_tensor is counts_sliced_padded
return output_tensor
monkeypatch.setattr(ttnn, "slice", slice_tensor)
monkeypatch.setattr(
ttnn,
"gt",
lambda tensor, threshold, *, output_tensor, **kwargs: events.append(
("gt", tensor, threshold, output_tensor, kwargs)
)
or output_tensor,
)
returned_counts, returned_mask = pen._token_bin_counts_and_mask(
new_tokens,
object(),
counts=counts,
mask=mask,
counts_sliced=counts_sliced,
)
assert returned_counts is counts
assert returned_mask is mask
assert events[-1] == ("gt", counts_sliced, 0, mask, {})
slices = [event for event in events if isinstance(event, tuple) and event[0] == "slice"]
assert slices == [
(
"slice",
counts_padded,
pen._slice_start,
pen._slice_end,
counts_sliced_padded,
1,
8,
{},
)
]
@pytest.mark.parametrize("ttnn_mesh_device", [(1, 1), (1, 2), (1, 8)], ids=["1x1", "1x2", "1x8"], indirect=True)
class TestPenalties1DDeviceExtra:
"""Coverage for methods not exercised by the reference tests."""
# ------------------------------------------------------------------
# _buf_resolved ttnn.Tensor path (line 95)
# ------------------------------------------------------------------
def test_buf_resolved_with_tt_tensor(self, ttnn_mesh_device):
"""_buf_resolved returns True for a real ttnn.Tensor (line 95)."""
tt = ttnn.from_torch(
torch.zeros(1, 1, dtype=torch.int32),
device=ttnn_mesh_device,
dtype=ttnn.int32,
layout=ttnn.TILE_LAYOUT,
)
assert Penalties1DConfig._buf_resolved(tt)
# ------------------------------------------------------------------
# from_config (lines 141-145)
# ------------------------------------------------------------------
@pytest.mark.parametrize("vocab_size", [1024])
def test_from_config(self, ttnn_mesh_device, vocab_size):
"""from_config power-path classmethod (lines 141-145)."""
cfg = Penalties1DConfig(vocab_size=vocab_size, mesh_device=ttnn_mesh_device)
pen = Penalties1D.from_config(cfg)
assert pen.config.vocab_size == vocab_size
assert pen.config.mesh_device is ttnn_mesh_device
assert not pen._device_buffers_loaded
# ------------------------------------------------------------------
# load_device_buffers idempotent guard (line 166)
# ------------------------------------------------------------------
@pytest.mark.parametrize("vocab_size", [1024])
def test_load_device_buffers_idempotent(self, ttnn_mesh_device, vocab_size):
"""Second call to load_device_buffers returns early without re-allocating (line 166)."""
pen = Penalties1D(vocab_size=vocab_size, mesh_device=ttnn_mesh_device)
pen.load_device_buffers()
decode_src_first = pen._decode_src
pen.load_device_buffers() # hits early-return at line 166
assert pen._decode_src is decode_src_first
# ------------------------------------------------------------------
# init_prompt_penalties + _token_bin_counts_and_mask counts=None path
# ------------------------------------------------------------------
@pytest.mark.parametrize("max_batch_size", [1, 32])
@pytest.mark.parametrize("vocab_size", [1024])
def test_init_prompt_penalties(self, ttnn_mesh_device, vocab_size, max_batch_size):
"""init_prompt_penalties scatters prompt tokens into prompt_mask."""
pen = Penalties1D(
vocab_size=vocab_size,
mesh_device=ttnn_mesh_device,
max_batch_size=max_batch_size,
)
pen.load_device_buffers()
params, accum = _make_proper_params_accum(pen)
prompt_tokens = torch.randint(0, vocab_size, (max_batch_size, 10))
pen.init_prompt_penalties(params, accum, prompt_tokens)
# ------------------------------------------------------------------
# forward() prompt init dispatch
# ------------------------------------------------------------------
@pytest.mark.parametrize("vocab_size", [1024])
def test_forward_dispatches_to_init_prompt(self, ttnn_mesh_device, vocab_size):
"""forward() with prompt_tokens kwarg routes to init_prompt_penalties."""
B = 32
pen = Penalties1D(vocab_size=vocab_size, mesh_device=ttnn_mesh_device)
pen.load_device_buffers()
params, accum = _make_proper_params_accum(pen)
logits_tt = ttnn.from_torch(
torch.randn(B, vocab_size, dtype=torch.bfloat16),
device=ttnn_mesh_device,
dtype=ttnn.bfloat16,
layout=ttnn.TILE_LAYOUT,
)
prompt_tokens = torch.randint(0, vocab_size, (B, 5))
result = pen.forward(logits_tt, params=params, accum=accum, prompt_tokens=prompt_tokens)
assert result is logits_tt
@pytest.mark.parametrize("vocab_size", [1024])
def test_forward_dispatches_to_decode(self, ttnn_mesh_device, vocab_size):
"""forward() without prompt_tokens routes to decode_forward (line 280).
Uses unsharded (replicated) tensors — same topology as test_penalties1d_vs_reference —
so the broadcast between logits [B, V] and penalty masks [B, V] is valid on all mesh
shapes. The goal here is line 280 coverage, not correctness (covered elsewhere).
"""
B = 32
pen = Penalties1D(vocab_size=vocab_size, mesh_device=ttnn_mesh_device)
zeros_BV = torch.zeros(B, vocab_size, dtype=torch.int32)
params, accum = _make_penalty_tensors_on_device(
ttnn_mesh_device,
B,
prompt_mask_host=zeros_BV,
output_mask_host=zeros_BV,
output_counts_host=zeros_BV,
presence_val=0.0,
frequency_val=0.0,
repetition_val=1.0,
)
logits_tt = ttnn.from_torch(
torch.randn(B, vocab_size, dtype=torch.bfloat16),
device=ttnn_mesh_device,
dtype=ttnn.bfloat16,
layout=ttnn.TILE_LAYOUT,
)
result = pen.forward(logits_tt, params=params, accum=accum)
assert result is not None
# ------------------------------------------------------------------
# update_output_tokens: standard decode path (lines 290-294)
# and _token_bin_counts_and_mask counts-not-None path (line 419)
# ------------------------------------------------------------------
@pytest.mark.parametrize("max_batch_size", [1, 32])
@pytest.mark.parametrize("vocab_size", [1024])
def test_update_output_tokens_standard(self, ttnn_mesh_device, vocab_size, max_batch_size):
"""update_output_tokens with standard decode-shape [1,1,1,B] (lines 290-294)."""
pen = Penalties1D(
vocab_size=vocab_size,
mesh_device=ttnn_mesh_device,
max_batch_size=max_batch_size,
)
pen.load_device_buffers()
_, accum = _make_proper_params_accum(pen)
# Standard sampling output: shape[-1]=B, shape[-2]=1 → if-branch.
tokens_tt = ttnn.from_torch(
torch.randint(0, vocab_size, (1, 1, 1, max_batch_size), dtype=torch.int32),
device=ttnn_mesh_device,
dtype=ttnn.int32,
layout=ttnn.ROW_MAJOR_LAYOUT,
)
pen.update_output_tokens(accum, tokens_tt)
# ------------------------------------------------------------------
# update_output_tokens: multi-token else branch (lines 296-303)
# ------------------------------------------------------------------
@pytest.mark.parametrize("vocab_size", [1024])
def test_update_output_tokens_multi_token(self, ttnn_mesh_device, vocab_size):
"""update_output_tokens with multi-token [B,S] shape triggers else-branch (lines 296-303)."""
B = 32
pen = Penalties1D(vocab_size=vocab_size, mesh_device=ttnn_mesh_device)
pen.load_device_buffers()
_, accum = _make_proper_params_accum(pen)
# shape[-1]=4 != B=32 → else-branch; src = ones(B, 4) created inline
tokens_tt = ttnn.from_torch(
torch.randint(0, vocab_size, (B, 4), dtype=torch.int32),
device=ttnn_mesh_device,
dtype=ttnn.int32,
layout=ttnn.ROW_MAJOR_LAYOUT,
)
pen.update_output_tokens(accum, tokens_tt)
# ------------------------------------------------------------------
# reset_output_tokens: tokens=None (lines 317-324) and with tokens (lines 326-346)
# ------------------------------------------------------------------
@pytest.mark.parametrize("vocab_size", [1024])
def test_reset_output_tokens_no_tokens(self, ttnn_mesh_device, vocab_size):
"""reset_output_tokens(tokens=None) zeros the accum buffers (lines 317-324)."""
pen = Penalties1D(vocab_size=vocab_size, mesh_device=ttnn_mesh_device)
pen.load_device_buffers()
_, accum = _make_proper_params_accum(pen)
pen.reset_output_tokens(accum, tokens=None)
@pytest.mark.parametrize("vocab_size", [1024])
def test_reset_output_tokens_with_tokens(self, ttnn_mesh_device, vocab_size):
"""reset_output_tokens(tokens=...) zeros then re-initializes from tokens (lines 326-346)."""
B = 32
pen = Penalties1D(vocab_size=vocab_size, mesh_device=ttnn_mesh_device)
pen.load_device_buffers()
_, accum = _make_proper_params_accum(pen)
tokens = torch.randint(0, vocab_size, (B, 5))
pen.reset_output_tokens(accum, tokens=tokens)
# ------------------------------------------------------------------
# _pad_batch_to_max: pad (lines 399-401), truncate (402-403), ValueError (396-397)
# ------------------------------------------------------------------
def test_pad_batch_to_max_pads_small_batch(self, ttnn_mesh_device):
"""_pad_batch_to_max pads when B < max_batch_size (lines 399-401)."""
pen = Penalties1D(vocab_size=1024, mesh_device=ttnn_mesh_device)
pen.load_device_buffers()
small = torch.randint(0, 100, (4, 10))
padded = pen._pad_batch_to_max(small, pad_value=-1)
assert padded.shape[0] == pen.config.max_batch_size
assert (padded[4:] == -1).all()
def test_pad_batch_to_max_truncates_large_batch(self, ttnn_mesh_device):
"""_pad_batch_to_max truncates when B > max_batch_size (lines 402-403)."""
pen = Penalties1D(vocab_size=1024, mesh_device=ttnn_mesh_device)
pen.load_device_buffers()
large = torch.randint(0, 100, (64, 10))
truncated = pen._pad_batch_to_max(large, pad_value=-1)
assert truncated.shape[0] == pen.config.max_batch_size
def test_pad_batch_to_max_raises_on_non_2d(self, ttnn_mesh_device):
"""_pad_batch_to_max raises ValueError for non-2D input (lines 396-397)."""
pen = Penalties1D(vocab_size=1024, mesh_device=ttnn_mesh_device)
pen.load_device_buffers()
with pytest.raises(ValueError, match="Expected 2D"): # allow-pytest.raises: pre-existing
pen._pad_batch_to_max(torch.zeros(10), pad_value=-1)
# ------------------------------------------------------------------
# _resolve_buf: ttnn.Tensor passthrough (lines 474-475)
# and LazyBuffer resolve (line 476)
# ------------------------------------------------------------------
@pytest.mark.parametrize("vocab_size", [1024])
def test_resolve_buf_tensor_passthrough(self, ttnn_mesh_device, vocab_size):
"""Pre-existing ttnn.Tensor passes through _resolve_buf unchanged (lines 474-475)."""
B = 32
pre_tensor = ttnn.from_torch(
torch.zeros(B, vocab_size, dtype=torch.int32),
device=ttnn_mesh_device,
dtype=ttnn.int32,
layout=ttnn.TILE_LAYOUT,
)
cfg = Penalties1DConfig(vocab_size=vocab_size, mesh_device=ttnn_mesh_device, prompt_mask=pre_tensor)
resolved = _resolve_penalties1d_config(cfg)
assert resolved.prompt_mask is pre_tensor
@pytest.mark.parametrize("vocab_size", [1024])
def test_resolve_buf_lazy_buffer_passthrough(self, ttnn_mesh_device, vocab_size):
"""Pre-existing LazyBuffer with device=None gets device filled in (line 476)."""
from models.common.modules.lazy_buffer import LazyBuffer
B = 32
partial_lb = LazyBuffer(
source=torch.zeros(B, vocab_size, dtype=torch.int32),
dtype=ttnn.int32,
layout=ttnn.TILE_LAYOUT,
device=None,
memory_config=ttnn.DRAM_MEMORY_CONFIG,
)
cfg = Penalties1DConfig(vocab_size=vocab_size, mesh_device=ttnn_mesh_device, prompt_mask=partial_lb)
resolved = _resolve_penalties1d_config(cfg)
assert isinstance(resolved.prompt_mask, LazyBuffer)
assert resolved.prompt_mask.device is ttnn_mesh_device
# ------------------------------------------------------------------
# _materialize: ttnn.Tensor passthrough (line 552)
# ------------------------------------------------------------------
@pytest.mark.parametrize("vocab_size", [1024])
def test_materialize_tensor_passthrough(self, ttnn_mesh_device, vocab_size):
"""Pre-existing ttnn.Tensor as decode_src passes through _materialize (line 552)."""
B = 32
pre_src = ttnn.from_torch(
torch.ones(B, 1, dtype=torch.int32),
device=ttnn_mesh_device,
dtype=ttnn.int32,
layout=ttnn.ROW_MAJOR_LAYOUT,
)
cfg = Penalties1DConfig(vocab_size=vocab_size, mesh_device=ttnn_mesh_device, decode_src=pre_src)
pen = Penalties1D.from_config(cfg)
pen.load_device_buffers()
assert pen._decode_src is pre_src
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