char-fork/llama.cpp

git clone https://git.t4t.associates/char-fork/llama.cpp

Aaron Teoargs: refactor mlock/mmap/directio into load-mode (#20834)e6dd0e29a2026-07-23

#!/usr/bin/env python3

import argparse
import csv
import heapq
import json
import logging
import os
import sqlite3
import sys
from collections.abc import Iterator, Sequence
from glob import glob
from typing import Any, Optional, Union

try:
    import git
    from tabulate import tabulate
except ImportError as e:
    print("the following Python libraries are required: GitPython, tabulate.") # noqa: NP100
    raise e


logger = logging.getLogger("compare-llama-bench")

# All llama-bench SQL fields
LLAMA_BENCH_DB_FIELDS = [
    "build_commit", "build_number", "cpu_info",       "gpu_info",   "backends",     "model_filename",
    "model_type",   "model_size",   "model_n_params", "n_batch",    "n_ubatch",     "n_threads",
    "cpu_mask",     "cpu_strict",   "poll",           "type_k",     "type_v",       "n_gpu_layers",
    "split_mode",   "main_gpu",     "no_kv_offload",  "flash_attn", "tensor_split", "tensor_buft_overrides",
    "load_mode",    "embeddings",   "no_op_offload",  "n_prompt",   "n_gen",        "n_depth",
    "test_time",    "avg_ns",       "stddev_ns",      "avg_ts",     "stddev_ts",    "n_cpu_moe",
    "fit_target",   "fit_min_ctx"
]

LLAMA_BENCH_DB_TYPES = [
    "TEXT",    "INTEGER", "TEXT",    "TEXT",    "TEXT",    "TEXT",
    "TEXT",    "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER",
    "TEXT",    "INTEGER", "INTEGER", "TEXT",    "TEXT",    "INTEGER",
    "TEXT",    "INTEGER", "INTEGER", "INTEGER", "TEXT",    "TEXT",
    "TEXT", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER",
    "TEXT",    "INTEGER", "INTEGER", "REAL",    "REAL",    "INTEGER",
    "INTEGER", "INTEGER"
]

# All test-backend-ops SQL fields
TEST_BACKEND_OPS_DB_FIELDS = [
    "test_time", "build_commit", "backend_name",  "op_name", "op_params", "test_mode",
    "supported", "passed",       "error_message", "time_us", "flops",     "bandwidth_gb_s",
    "memory_kb", "n_runs"
]

TEST_BACKEND_OPS_DB_TYPES = [
    "TEXT",    "TEXT",    "TEXT", "TEXT", "TEXT", "TEXT",
    "INTEGER", "INTEGER", "TEXT", "REAL", "REAL", "REAL",
    "INTEGER", "INTEGER"
]

assert len(LLAMA_BENCH_DB_FIELDS) == len(LLAMA_BENCH_DB_TYPES)
assert len(TEST_BACKEND_OPS_DB_FIELDS) == len(TEST_BACKEND_OPS_DB_TYPES)

# Properties by which to differentiate results per commit for llama-bench:
LLAMA_BENCH_KEY_PROPERTIES = [
    "cpu_info", "gpu_info", "backends", "n_gpu_layers", "n_cpu_moe", "tensor_buft_overrides", "model_filename", "model_type",
    "n_batch", "n_ubatch", "embeddings", "cpu_mask", "cpu_strict", "poll", "n_threads", "type_k", "type_v",
    "load_mode", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen", "n_depth",
    "fit_target", "fit_min_ctx"
]

# Properties by which to differentiate results per commit for test-backend-ops:
TEST_BACKEND_OPS_KEY_PROPERTIES = [
    "backend_name", "op_name", "op_params", "test_mode"
]

# Properties that are boolean and are converted to Yes/No for the table:
LLAMA_BENCH_BOOL_PROPERTIES = ["embeddings", "cpu_strict", "no_kv_offload", "flash_attn"]
TEST_BACKEND_OPS_BOOL_PROPERTIES = ["supported", "passed"]

# Header names for the table (llama-bench):
LLAMA_BENCH_PRETTY_NAMES = {
    "cpu_info": "CPU", "gpu_info": "GPU", "backends": "Backends", "n_gpu_layers": "GPU layers",
    "tensor_buft_overrides": "Tensor overrides", "model_filename": "File", "model_type": "Model", "model_size": "Model size [GiB]",
    "model_n_params": "Num. of par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size", "embeddings": "Embeddings",
    "cpu_mask": "CPU mask", "cpu_strict": "CPU strict", "poll": "Poll", "n_threads": "Threads", "type_k": "K type", "type_v": "V type",
    "load_mode": "Load mode", "no_kv_offload": "NKVO", "split_mode": "Split mode", "main_gpu": "Main GPU", "tensor_split": "Tensor split",
    "flash_attn": "FlashAttention",
}

# Header names for the table (test-backend-ops):
TEST_BACKEND_OPS_PRETTY_NAMES = {
    "backend_name": "Backend", "op_name": "GGML op", "op_params": "Op parameters", "test_mode": "Mode",
    "supported": "Supported", "passed": "Passed", "error_message": "Error",
    "flops": "FLOPS", "bandwidth_gb_s": "Bandwidth (GB/s)", "memory_kb": "Memory (KB)", "n_runs": "Runs"
}

DEFAULT_SHOW_LLAMA_BENCH = ["model_type"]  # Always show these properties by default.
DEFAULT_HIDE_LLAMA_BENCH = ["model_filename"]  # Always hide these properties by default.

DEFAULT_SHOW_TEST_BACKEND_OPS = ["backend_name", "op_name"]  # Always show these properties by default.
DEFAULT_HIDE_TEST_BACKEND_OPS = ["error_message"]  # Always hide these properties by default.

GPU_NAME_STRIP = ["NVIDIA GeForce ", "Tesla ", "AMD Radeon ", "AMD Instinct "]  # Strip prefixes for smaller tables.
MODEL_SUFFIX_REPLACE = {" - Small": "_S", " - Medium": "_M", " - Large": "_L"}

DESCRIPTION = """Creates tables from llama-bench or test-backend-ops data written to multiple JSON/CSV files, a single JSONL file or SQLite database. Example usage (Linux):

For llama-bench:
$ git checkout master
$ cmake -B ${BUILD_DIR} ${CMAKE_OPTS} && cmake --build ${BUILD_DIR} -t llama-bench -j $(nproc)
$ ./llama-bench -o sql | sqlite3 llama-bench.sqlite
$ git checkout some_branch
$ cmake -B ${BUILD_DIR} ${CMAKE_OPTS} && cmake --build ${BUILD_DIR} -t llama-bench -j $(nproc)
$ ./llama-bench -o sql | sqlite3 llama-bench.sqlite
$ ./scripts/compare-llama-bench.py

For test-backend-ops:
$ git checkout master
$ cmake -B ${BUILD_DIR} ${CMAKE_OPTS} && cmake --build ${BUILD_DIR} -t test-backend-ops -j $(nproc)
$ ./test-backend-ops perf --output sql | sqlite3 test-backend-ops.sqlite
$ git checkout some_branch
$ cmake -B ${BUILD_DIR} ${CMAKE_OPTS} && cmake --build ${BUILD_DIR} -t test-backend-ops -j $(nproc)
$ ./test-backend-ops perf --output sql | sqlite3 test-backend-ops.sqlite
$ ./scripts/compare-llama-bench.py --tool test-backend-ops -i test-backend-ops.sqlite

Performance numbers from multiple runs per commit are averaged WITHOUT being weighted by the --repetitions parameter of llama-bench.
"""

parser = argparse.ArgumentParser(
    description=DESCRIPTION, formatter_class=argparse.RawDescriptionHelpFormatter)
help_b = (
    "The baseline commit to compare performance to. "
    "Accepts either a branch name, tag name, or commit hash. "
    "Defaults to latest master commit with data."
)
parser.add_argument("-b", "--baseline", help=help_b)
help_c = (
    "The commit whose performance is to be compared to the baseline. "
    "Accepts either a branch name, tag name, or commit hash. "
    "Defaults to the non-master commit for which llama-bench was run most recently."
)
parser.add_argument("-c", "--compare", help=help_c)
help_t = (
    "The tool whose data is being compared. "
    "Either 'llama-bench' or 'test-backend-ops'. "
    "This determines the database schema and comparison logic used. "
    "If left unspecified, try to determine from the input file."
)
parser.add_argument("-t", "--tool", help=help_t, default=None, choices=[None, "llama-bench", "test-backend-ops"])
help_i = (
    "JSON/JSONL/SQLite/CSV files for comparing commits. "
    "Specify multiple times to use multiple input files (JSON/CSV only). "
    "Defaults to 'llama-bench.sqlite' in the current working directory. "
    "If no such file is found and there is exactly one .sqlite file in the current directory, "
    "that file is instead used as input."
)
parser.add_argument("-i", "--input", action="append", help=help_i)
help_o = (
    "Output format for the table. "
    "Defaults to 'pipe' (GitHub compatible). "
    "Also supports e.g. 'latex' or 'mediawiki'. "
    "See tabulate documentation for full list."
)
parser.add_argument("-o", "--output", help=help_o, default="pipe")
help_s = (
    "Columns to add to the table. "
    "Accepts a comma-separated list of values. "
    f"Legal values for test-backend-ops: {', '.join(TEST_BACKEND_OPS_KEY_PROPERTIES)}. "
    f"Legal values for llama-bench: {', '.join(LLAMA_BENCH_KEY_PROPERTIES[:-3])}. "
    "Defaults to model name (model_type) and CPU and/or GPU name (cpu_info, gpu_info) "
    "plus any column where not all data points are the same. "
    "If the columns are manually specified, then the results for each unique combination of the "
    "specified values are averaged WITHOUT weighing by the --repetitions parameter of llama-bench."
)
parser.add_argument("--check", action="store_true", help="check if all required Python libraries are installed")
parser.add_argument("-s", "--show", help=help_s)
parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
parser.add_argument("--plot", help="generate a performance comparison plot and save to specified file (e.g., plot.png)")
parser.add_argument("--plot_x", help="parameter to use as x axis for plotting (default: n_depth)", default="n_depth")
parser.add_argument("--plot_log_scale", action="store_true", help="use log scale for x axis in plots (off by default)")

known_args, unknown_args = parser.parse_known_args()

logging.basicConfig(level=logging.DEBUG if known_args.verbose else logging.INFO)


if known_args.check:
    # Check if all required Python libraries are installed. Would have failed earlier if not.
    sys.exit(0)

if unknown_args:
    logger.error(f"Received unknown args: {unknown_args}.\n")
    parser.print_help()
    sys.exit(1)

input_file = known_args.input
tool = known_args.tool

if not input_file:
    if tool == "llama-bench" and os.path.exists("./llama-bench.sqlite"):
        input_file = ["llama-bench.sqlite"]
    elif tool == "test-backend-ops" and os.path.exists("./test-backend-ops.sqlite"):
        input_file = ["test-backend-ops.sqlite"]

if not input_file:
    sqlite_files = glob("*.sqlite")
    if len(sqlite_files) == 1:
        input_file = sqlite_files

if not input_file:
    logger.error("Cannot find a suitable input file, please provide one.\n")
    parser.print_help()
    sys.exit(1)


class LlamaBenchData:
    repo: Optional[git.Repo]
    build_len_min: int
    build_len_max: int
    build_len: int = 8
    builds: list[str] = []
    tool: str = "llama-bench"  # Tool type: "llama-bench" or "test-backend-ops"

    def __init__(self, tool: str = "llama-bench"):
        self.tool = tool
        try:
            self.repo = git.Repo(".", search_parent_directories=True)
        except git.InvalidGitRepositoryError:
            self.repo = None

        # Set schema-specific properties based on tool
        if self.tool == "llama-bench":
            self.check_keys = set(LLAMA_BENCH_KEY_PROPERTIES + ["build_commit", "test_time", "avg_ts"])
        elif self.tool == "test-backend-ops":
            self.check_keys = set(TEST_BACKEND_OPS_KEY_PROPERTIES + ["build_commit", "test_time"])
        else:
            assert False

    def _builds_init(self):
        self.build_len = self.build_len_min

    def _check_keys(self, keys: set) -> Optional[set]:
        """Private helper method that checks against required data keys and returns missing ones."""
        if not keys >= self.check_keys:
            return self.check_keys - keys
        return None

    def find_parent_in_data(self, commit: git.Commit) -> Optional[str]:
        """Helper method to find the most recent parent measured in number of commits for which there is data."""
        heap: list[tuple[int, git.Commit]] = [(0, commit)]
        seen_hexsha8 = set()
        while heap:
            depth, current_commit = heapq.heappop(heap)
            current_hexsha8 = commit.hexsha[:self.build_len]
            if current_hexsha8 in self.builds:
                return current_hexsha8
            for parent in commit.parents:
                parent_hexsha8 = parent.hexsha[:self.build_len]
                if parent_hexsha8 not in seen_hexsha8:
                    seen_hexsha8.add(parent_hexsha8)
                    heapq.heappush(heap, (depth + 1, parent))
        return None

    def get_all_parent_hexsha8s(self, commit: git.Commit) -> Sequence[str]:
        """Helper method to recursively get hexsha8 values for all parents of a commit."""
        unvisited = [commit]
        visited   = []

        while unvisited:
            current_commit = unvisited.pop(0)
            visited.append(current_commit.hexsha[:self.build_len])
            for parent in current_commit.parents:
                if parent.hexsha[:self.build_len] not in visited:
                    unvisited.append(parent)

        return visited

    def get_commit_name(self, hexsha8: str) -> str:
        """Helper method to find a human-readable name for a commit if possible."""
        if self.repo is None:
            return hexsha8
        for h in self.repo.heads:
            if h.commit.hexsha[:self.build_len] == hexsha8:
                return h.name
        for t in self.repo.tags:
            if t.commit.hexsha[:self.build_len] == hexsha8:
                return t.name
        return hexsha8

    def get_commit_hexsha8(self, name: str) -> Optional[str]:
        """Helper method to search for a commit given a human-readable name."""
        if self.repo is None:
            return None
        for h in self.repo.heads:
            if h.name == name:
                return h.commit.hexsha[:self.build_len]
        for t in self.repo.tags:
            if t.name == name:
                return t.commit.hexsha[:self.build_len]
        for remote in self.repo.remotes:
            for ref in remote.refs:
                if ref.name == name or ref.remote_head == name:
                    return ref.commit.hexsha[:self.build_len]
        for c in self.repo.iter_commits("--all"):
            if c.hexsha[:self.build_len] == name[:self.build_len]:
                return c.hexsha[:self.build_len]
        return None

    def builds_timestamp(self, reverse: bool = False) -> Union[Iterator[tuple], Sequence[tuple]]:
        """Helper method that gets rows of (build_commit, test_time) sorted by the latter."""
        return []

    def get_rows(self, properties: list[str], hexsha8_baseline: str, hexsha8_compare: str) -> Sequence[tuple]:
        """
        Helper method that gets table rows for some list of properties.
        Rows are created by combining those where all provided properties are equal.
        The resulting rows are then grouped by the provided properties and the t/s values are averaged.
        The returned rows are unique in terms of property combinations.
        """
        return []


class LlamaBenchDataSQLite3(LlamaBenchData):
    connection: Optional[sqlite3.Connection] = None
    cursor: sqlite3.Cursor
    table_name: str

    def __init__(self, tool: str = "llama-bench"):
        super().__init__(tool)
        if self.connection is None:
            self.connection = sqlite3.connect(":memory:")
            self.cursor = self.connection.cursor()

            # Set table name and schema based on tool
            if self.tool == "llama-bench":
                self.table_name = "llama_bench"
                db_fields = LLAMA_BENCH_DB_FIELDS
                db_types = LLAMA_BENCH_DB_TYPES
            elif self.tool == "test-backend-ops":
                self.table_name = "test_backend_ops"
                db_fields = TEST_BACKEND_OPS_DB_FIELDS
                db_types = TEST_BACKEND_OPS_DB_TYPES
            else:
                assert False

            self.cursor.execute(f"CREATE TABLE {self.table_name}({', '.join(' '.join(x) for x in zip(db_fields, db_types))});")

    def _builds_init(self):
        if self.connection:
            self.build_len_min = self.cursor.execute(f"SELECT MIN(LENGTH(build_commit)) from {self.table_name};").fetchone()[0]
            self.build_len_max = self.cursor.execute(f"SELECT MAX(LENGTH(build_commit)) from {self.table_name};").fetchone()[0]

            if self.build_len_min != self.build_len_max:
                logger.warning("Data contains commit hashes of differing lengths. It's possible that the wrong commits will be compared. "
                               "Try purging the the database of old commits.")
                self.cursor.execute(f"UPDATE {self.table_name} SET build_commit = SUBSTRING(build_commit, 1, {self.build_len_min});")

            builds = self.cursor.execute(f"SELECT DISTINCT build_commit FROM {self.table_name};").fetchall()
            self.builds = list(map(lambda b: b[0], builds))  # list[tuple[str]] -> list[str]
        super()._builds_init()

    def builds_timestamp(self, reverse: bool = False) -> Union[Iterator[tuple], Sequence[tuple]]:
        data = self.cursor.execute(
            f"SELECT build_commit, test_time FROM {self.table_name} ORDER BY test_time;").fetchall()
        return reversed(data) if reverse else data

    def get_rows(self, properties: list[str], hexsha8_baseline: str, hexsha8_compare: str) -> Sequence[tuple]:
        if self.tool == "llama-bench":
            return self._get_rows_llama_bench(properties, hexsha8_baseline, hexsha8_compare)
        elif self.tool == "test-backend-ops":
            return self._get_rows_test_backend_ops(properties, hexsha8_baseline, hexsha8_compare)
        else:
            assert False

    def _get_rows_llama_bench(self, properties: list[str], hexsha8_baseline: str, hexsha8_compare: str) -> Sequence[tuple]:
        select_string = ", ".join(
            [f"tb.{p}" for p in properties] + ["tb.n_prompt", "tb.n_gen", "tb.n_depth", "AVG(tb.avg_ts)", "AVG(tc.avg_ts)"])
        equal_string = " AND ".join(
            [f"tb.{p} = tc.{p}" for p in LLAMA_BENCH_KEY_PROPERTIES] + [
                f"tb.build_commit = '{hexsha8_baseline}'", f"tc.build_commit = '{hexsha8_compare}'"]
        )
        group_order_string = ", ".join([f"tb.{p}" for p in properties] + ["tb.n_gen", "tb.n_prompt", "tb.n_depth"])
        query = (f"SELECT {select_string} FROM {self.table_name} tb JOIN {self.table_name} tc ON {equal_string} "
                 f"GROUP BY {group_order_string} ORDER BY {group_order_string};")
        return self.cursor.execute(query).fetchall()

    def _get_rows_test_backend_ops(self, properties: list[str], hexsha8_baseline: str, hexsha8_compare: str) -> Sequence[tuple]:
        # For test-backend-ops, we compare FLOPS and bandwidth metrics (prioritizing FLOPS over bandwidth)
        select_string = ", ".join(
            [f"tb.{p}" for p in properties] + [
                "AVG(tb.flops)", "AVG(tc.flops)",
                "AVG(tb.bandwidth_gb_s)", "AVG(tc.bandwidth_gb_s)"
            ])
        equal_string = " AND ".join(
            [f"tb.{p} = tc.{p}" for p in TEST_BACKEND_OPS_KEY_PROPERTIES] + [
                f"tb.build_commit = '{hexsha8_baseline}'", f"tc.build_commit = '{hexsha8_compare}'",
                "tb.supported = 1", "tc.supported = 1", "tb.passed = 1", "tc.passed = 1"]  # Only compare successful tests
        )
        group_order_string = ", ".join([f"tb.{p}" for p in properties])
        query = (f"SELECT {select_string} FROM {self.table_name} tb JOIN {self.table_name} tc ON {equal_string} "
                 f"GROUP BY {group_order_string} ORDER BY {group_order_string};")
        return self.cursor.execute(query).fetchall()


class LlamaBenchDataSQLite3File(LlamaBenchDataSQLite3):
    def __init__(self, data_file: str, tool: Any):
        self.connection = sqlite3.connect(data_file)
        self.cursor = self.connection.cursor()

        # Check which table exists in the database
        tables = self.cursor.execute("SELECT name FROM sqlite_master WHERE type='table';").fetchall()
        table_names = [table[0] for table in tables]

        # Tool selection logic
        if tool is None:
            if "llama_bench" in table_names:
                self.table_name = "llama_bench"
                tool = "llama-bench"
            elif "test_backend_ops" in table_names:
                self.table_name = "test_backend_ops"
                tool = "test-backend-ops"
            else:
                raise RuntimeError(f"No suitable table found in database. Available tables: {table_names}")
        elif tool == "llama-bench":
            if "llama_bench" in table_names:
                self.table_name = "llama_bench"
                tool = "llama-bench"
            else:
                raise RuntimeError(f"Table 'test' not found for tool 'llama-bench'. Available tables: {table_names}")
        elif tool == "test-backend-ops":
            if "test_backend_ops" in table_names:
                self.table_name = "test_backend_ops"
                tool = "test-backend-ops"
            else:
                raise RuntimeError(f"Table 'test_backend_ops' not found for tool 'test-backend-ops'. Available tables: {table_names}")
        else:
            raise RuntimeError(f"Unknown tool: {tool}")

        super().__init__(tool)
        self._builds_init()

    @staticmethod
    def valid_format(data_file: str) -> bool:
        connection = sqlite3.connect(data_file)
        cursor = connection.cursor()

        try:
            if cursor.execute("PRAGMA schema_version;").fetchone()[0] == 0:
                raise sqlite3.DatabaseError("The provided input file does not exist or is empty.")
        except sqlite3.DatabaseError as e:
            logger.debug(f'"{data_file}" is not a valid SQLite3 file.', exc_info=e)
            cursor = None

        connection.close()
        return True if cursor else False


class LlamaBenchDataJSONL(LlamaBenchDataSQLite3):
    def __init__(self, data_file: str, tool: str = "llama-bench"):
        super().__init__(tool)

        # Get the appropriate field list based on tool
        db_fields = LLAMA_BENCH_DB_FIELDS if tool == "llama-bench" else TEST_BACKEND_OPS_DB_FIELDS

        with open(data_file, "r", encoding="utf-8") as fp:
            for i, line in enumerate(fp):
                parsed = json.loads(line)

                for k in parsed.keys() - set(db_fields):
                    del parsed[k]

                if (missing_keys := self._check_keys(parsed.keys())):
                    raise RuntimeError(f"Missing required data key(s) at line {i + 1}: {', '.join(missing_keys)}")

                self.cursor.execute(f"INSERT INTO {self.table_name}({', '.join(parsed.keys())}) VALUES({', '.join('?' * len(parsed))});", tuple(parsed.values()))

        self._builds_init()

    @staticmethod
    def valid_format(data_file: str) -> bool:
        try:
            with open(data_file, "r", encoding="utf-8") as fp:
                for line in fp:
                    json.loads(line)
                    break
        except Exception as e:
            logger.debug(f'"{data_file}" is not a valid JSONL file.', exc_info=e)
            return False

        return True


class LlamaBenchDataJSON(LlamaBenchDataSQLite3):
    def __init__(self, data_files: list[str], tool: str = "llama-bench"):
        super().__init__(tool)

        # Get the appropriate field list based on tool
        db_fields = LLAMA_BENCH_DB_FIELDS if tool == "llama-bench" else TEST_BACKEND_OPS_DB_FIELDS

        for data_file in data_files:
            with open(data_file, "r", encoding="utf-8") as fp:
                parsed = json.load(fp)

                for i, entry in enumerate(parsed):
                    for k in entry.keys() - set(db_fields):
                        del entry[k]

                    if (missing_keys := self._check_keys(entry.keys())):
                        raise RuntimeError(f"Missing required data key(s) at entry {i + 1}: {', '.join(missing_keys)}")

                    self.cursor.execute(f"INSERT INTO {self.table_name}({', '.join(entry.keys())}) VALUES({', '.join('?' * len(entry))});", tuple(entry.values()))

        self._builds_init()

    @staticmethod
    def valid_format(data_files: list[str]) -> bool:
        if not data_files:
            return False

        for data_file in data_files:
            try:
                with open(data_file, "r", encoding="utf-8") as fp:
                    json.load(fp)
            except Exception as e:
                logger.debug(f'"{data_file}" is not a valid JSON file.', exc_info=e)
                return False

        return True


class LlamaBenchDataCSV(LlamaBenchDataSQLite3):
    def __init__(self, data_files: list[str], tool: str = "llama-bench"):
        super().__init__(tool)

        # Get the appropriate field list based on tool
        db_fields = LLAMA_BENCH_DB_FIELDS if tool == "llama-bench" else TEST_BACKEND_OPS_DB_FIELDS

        for data_file in data_files:
            with open(data_file, "r", encoding="utf-8") as fp:
                for i, parsed in enumerate(csv.DictReader(fp)):
                    keys = set(parsed.keys())

                    for k in keys - set(db_fields):
                        del parsed[k]

                    if (missing_keys := self._check_keys(keys)):
                        raise RuntimeError(f"Missing required data key(s) at line {i + 1}: {', '.join(missing_keys)}")

                    self.cursor.execute(f"INSERT INTO {self.table_name}({', '.join(parsed.keys())}) VALUES({', '.join('?' * len(parsed))});", tuple(parsed.values()))

        self._builds_init()

    @staticmethod
    def valid_format(data_files: list[str]) -> bool:
        if not data_files:
            return False

        for data_file in data_files:
            try:
                with open(data_file, "r", encoding="utf-8") as fp:
                    for parsed in csv.DictReader(fp):
                        break
            except Exception as e:
                logger.debug(f'"{data_file}" is not a valid CSV file.', exc_info=e)
                return False

        return True


def format_flops(flops_value: float) -> str:
    """Format FLOPS values with appropriate units for better readability."""
    if flops_value == 0:
        return "0.00"

    # Define unit thresholds and names
    units = [
        (1e12, "T"),   # TeraFLOPS
        (1e9, "G"),    # GigaFLOPS
        (1e6, "M"),    # MegaFLOPS
        (1e3, "k"),    # kiloFLOPS
        (1, "")        # FLOPS
    ]

    for threshold, unit in units:
        if abs(flops_value) >= threshold:
            formatted_value = flops_value / threshold
            if formatted_value >= 100:
                return f"{formatted_value:.1f}{unit}"
            else:
                return f"{formatted_value:.2f}{unit}"

    # Fallback for very small values
    return f"{flops_value:.2f}"


def format_flops_for_table(flops_value: float, target_unit: str) -> str:
    """Format FLOPS values for table display without unit suffix (since unit is in header)."""
    if flops_value == 0:
        return "0.00"

    # Define unit thresholds based on target unit
    unit_divisors = {
        "TFLOPS": 1e12,
        "GFLOPS": 1e9,
        "MFLOPS": 1e6,
        "kFLOPS": 1e3,
        "FLOPS": 1
    }

    divisor = unit_divisors.get(target_unit, 1)
    formatted_value = flops_value / divisor

    if formatted_value >= 100:
        return f"{formatted_value:.1f}"
    else:
        return f"{formatted_value:.2f}"


def get_flops_unit_name(flops_values: list) -> str:
    """Determine the best FLOPS unit name based on the magnitude of values."""
    if not flops_values or all(v == 0 for v in flops_values):
        return "FLOPS"

    # Find the maximum absolute value to determine appropriate unit
    max_flops = max(abs(v) for v in flops_values if v != 0)

    if max_flops >= 1e12:
        return "TFLOPS"
    elif max_flops >= 1e9:
        return "GFLOPS"
    elif max_flops >= 1e6:
        return "MFLOPS"
    elif max_flops >= 1e3:
        return "kFLOPS"
    else:
        return "FLOPS"


bench_data = None
if len(input_file) == 1:
    if LlamaBenchDataSQLite3File.valid_format(input_file[0]):
        bench_data = LlamaBenchDataSQLite3File(input_file[0], tool)
    elif LlamaBenchDataJSON.valid_format(input_file):
        bench_data = LlamaBenchDataJSON(input_file, tool)
    elif LlamaBenchDataJSONL.valid_format(input_file[0]):
        bench_data = LlamaBenchDataJSONL(input_file[0], tool)
    elif LlamaBenchDataCSV.valid_format(input_file):
        bench_data = LlamaBenchDataCSV(input_file, tool)
else:
    if LlamaBenchDataJSON.valid_format(input_file):
        bench_data = LlamaBenchDataJSON(input_file, tool)
    elif LlamaBenchDataCSV.valid_format(input_file):
        bench_data = LlamaBenchDataCSV(input_file, tool)

if not bench_data:
    raise RuntimeError("No valid (or some invalid) input files found.")

if not bench_data.builds:
    raise RuntimeError(f"{input_file} does not contain any builds.")

tool = bench_data.tool  # May have chosen a default if tool was None.


hexsha8_baseline = name_baseline = None

# If the user specified a baseline, try to find a commit for it:
if known_args.baseline is not None:
    if known_args.baseline in bench_data.builds:
        hexsha8_baseline = known_args.baseline
    if hexsha8_baseline is None:
        hexsha8_baseline = bench_data.get_commit_hexsha8(known_args.baseline)
        name_baseline = known_args.baseline
    if hexsha8_baseline is None:
        logger.error(f"cannot find data for baseline={known_args.baseline}.")
        sys.exit(1)
# Otherwise, search for the most recent parent of master for which there is data:
elif bench_data.repo is not None:
    hexsha8_baseline = bench_data.find_parent_in_data(bench_data.repo.heads.master.commit)

    if hexsha8_baseline is None:
        logger.error("No baseline was provided and did not find data for any master branch commits.\n")
        parser.print_help()
        sys.exit(1)
else:
    logger.error("No baseline was provided and the current working directory "
                 "is not part of a git repository from which a baseline could be inferred.\n")
    parser.print_help()
    sys.exit(1)


assert isinstance(hexsha8_baseline, str)
name_baseline = bench_data.get_commit_name(hexsha8_baseline)

hexsha8_compare = name_compare = None

# If the user has specified a compare value, try to find a corresponding commit:
if known_args.compare is not None:
    if known_args.compare in bench_data.builds:
        hexsha8_compare = known_args.compare
    if hexsha8_compare is None:
        hexsha8_compare = bench_data.get_commit_hexsha8(known_args.compare)
        name_compare = known_args.compare
    if hexsha8_compare is None:
        logger.error(f"cannot find data for compare={known_args.compare}.")
        sys.exit(1)
# Otherwise, search for the commit for llama-bench was most recently run
# and that is not a parent of master:
elif bench_data.repo is not None:
    hexsha8s_master = bench_data.get_all_parent_hexsha8s(bench_data.repo.heads.master.commit)
    for (hexsha8, _) in bench_data.builds_timestamp(reverse=True):
        if hexsha8 not in hexsha8s_master:
            hexsha8_compare = hexsha8
            break

    if hexsha8_compare is None:
        logger.error("No compare target was provided and did not find data for any non-master commits.\n")
        parser.print_help()
        sys.exit(1)
else:
    logger.error("No compare target was provided and the current working directory "
                 "is not part of a git repository from which a compare target could be inferred.\n")
    parser.print_help()
    sys.exit(1)

assert isinstance(hexsha8_compare, str)
name_compare = bench_data.get_commit_name(hexsha8_compare)

# Get tool-specific configuration
if tool == "llama-bench":
    key_properties = LLAMA_BENCH_KEY_PROPERTIES
    bool_properties = LLAMA_BENCH_BOOL_PROPERTIES
    pretty_names = LLAMA_BENCH_PRETTY_NAMES
    default_show = DEFAULT_SHOW_LLAMA_BENCH
    default_hide = DEFAULT_HIDE_LLAMA_BENCH
elif tool == "test-backend-ops":
    key_properties = TEST_BACKEND_OPS_KEY_PROPERTIES
    bool_properties = TEST_BACKEND_OPS_BOOL_PROPERTIES
    pretty_names = TEST_BACKEND_OPS_PRETTY_NAMES
    default_show = DEFAULT_SHOW_TEST_BACKEND_OPS
    default_hide = DEFAULT_HIDE_TEST_BACKEND_OPS
else:
    assert False

# If the user provided columns to group the results by, use them:
if known_args.show is not None:
    show = known_args.show.split(",")
    unknown_cols = []
    for prop in show:
        valid_props = key_properties if tool == "test-backend-ops" else key_properties[:-3]  # Exclude n_prompt, n_gen, n_depth for llama-bench
        if prop not in valid_props:
            unknown_cols.append(prop)
    if unknown_cols:
        logger.error(f"Unknown values for --show: {', '.join(unknown_cols)}")
        parser.print_usage()
        sys.exit(1)
    rows_show = bench_data.get_rows(show, hexsha8_baseline, hexsha8_compare)
# Otherwise, select those columns where the values are not all the same:
else:
    rows_full = bench_data.get_rows(key_properties, hexsha8_baseline, hexsha8_compare)
    properties_different = []

    if tool == "llama-bench":
        # For llama-bench, skip n_prompt, n_gen, n_depth from differentiation logic
        check_properties = [kp for kp in key_properties if kp not in ["n_prompt", "n_gen", "n_depth"]]
        for i, kp_i in enumerate(key_properties):
            if kp_i in default_show or kp_i in ["n_prompt", "n_gen", "n_depth"]:
                continue
            for row_full in rows_full:
                if row_full[i] != rows_full[0][i]:
                    properties_different.append(kp_i)
                    break
    elif tool == "test-backend-ops":
        # For test-backend-ops, check all key properties
        for i, kp_i in enumerate(key_properties):
            if kp_i in default_show:
                continue
            for row_full in rows_full:
                if row_full[i] != rows_full[0][i]:
                    properties_different.append(kp_i)
                    break
    else:
        assert False

    show = []

    if tool == "llama-bench":
        # Show CPU and/or GPU by default even if the hardware for all results is the same:
        if rows_full and "n_gpu_layers" not in properties_different:
            ngl = int(rows_full[0][key_properties.index("n_gpu_layers")])

            if ngl != 99 and "cpu_info" not in properties_different:
                show.append("cpu_info")

        show += properties_different

        index_default = 0
        for prop in ["cpu_info", "gpu_info", "n_gpu_layers", "main_gpu"]:
            if prop in show:
                index_default += 1
        show = show[:index_default] + default_show + show[index_default:]
    elif tool == "test-backend-ops":
        show = default_show + properties_different
    else:
        assert False

    for prop in default_hide:
        try:
            show.remove(prop)
        except ValueError:
            pass

    # Add plot_x parameter to parameters to show if it's not already present:
    if known_args.plot:
        for k, v in pretty_names.items():
            if v == known_args.plot_x and k not in show:
                show.append(k)
                break

    rows_show = bench_data.get_rows(show, hexsha8_baseline, hexsha8_compare)

if not rows_show:
    logger.error(f"No comparable data was found between {name_baseline} and {name_compare}.\n")
    sys.exit(1)

table = []
primary_metric = "FLOPS"  # Default to FLOPS for test-backend-ops

if tool == "llama-bench":
    # For llama-bench, create test names and compare avg_ts values
    for row in rows_show:
        n_prompt = int(row[-5])
        n_gen    = int(row[-4])
        n_depth  = int(row[-3])
        if n_prompt != 0 and n_gen == 0:
            test_name = f"pp{n_prompt}"
        elif n_prompt == 0 and n_gen != 0:
            test_name = f"tg{n_gen}"
        else:
            test_name = f"pp{n_prompt}+tg{n_gen}"
        if n_depth != 0:
            test_name = f"{test_name}@d{n_depth}"
        #           Regular columns    test name    avg t/s values              Speedup
        #            VVVVVVVVVVVVV     VVVVVVVVV    VVVVVVVVVVVVVV              VVVVVVV
        table.append(list(row[:-5]) + [test_name] + list(row[-2:]) + [float(row[-1]) / float(row[-2])])
elif tool == "test-backend-ops":
    # Determine the primary metric by checking rows until we find one with valid data
    if rows_show:
        primary_metric = "FLOPS"  # Default to FLOPS
        flops_values = []

        # Collect all FLOPS values to determine the best unit
        for sample_row in rows_show:
            baseline_flops = float(sample_row[-4])
            compare_flops = float(sample_row[-3])
            baseline_bandwidth = float(sample_row[-2])

            if baseline_flops > 0:
                flops_values.extend([baseline_flops, compare_flops])
            elif baseline_bandwidth > 0 and not flops_values:
                primary_metric = "Bandwidth (GB/s)"

        # If we have FLOPS data, determine the appropriate unit
        if flops_values:
            primary_metric = get_flops_unit_name(flops_values)

    # For test-backend-ops, prioritize FLOPS > bandwidth for comparison
    for row in rows_show:
        # Extract metrics: flops, bandwidth_gb_s (baseline and compare)
        baseline_flops = float(row[-4])
        compare_flops = float(row[-3])
        baseline_bandwidth = float(row[-2])
        compare_bandwidth = float(row[-1])

        # Determine which metric to use for comparison (prioritize FLOPS > bandwidth)
        if baseline_flops > 0 and compare_flops > 0:
            # Use FLOPS comparison (higher is better)
            speedup = compare_flops / baseline_flops
            baseline_str = format_flops_for_table(baseline_flops, primary_metric)
            compare_str = format_flops_for_table(compare_flops, primary_metric)
        elif baseline_bandwidth > 0 and compare_bandwidth > 0:
            # Use bandwidth comparison (higher is better)
            speedup = compare_bandwidth / baseline_bandwidth
            baseline_str = f"{baseline_bandwidth:.2f}"
            compare_str = f"{compare_bandwidth:.2f}"
        else:
            # Fallback if no valid data is available
            baseline_str = "N/A"
            compare_str = "N/A"
            from math import nan
            speedup = nan

        table.append(list(row[:-4]) + [baseline_str, compare_str, speedup])
else:
    assert False

# Some a-posteriori fixes to make the table contents prettier:
for bool_property in bool_properties:
    if bool_property in show:
        ip = show.index(bool_property)
        for row_table in table:
            row_table[ip] = "Yes" if int(row_table[ip]) == 1 else "No"

if tool == "llama-bench":
    if "model_type" in show:
        ip = show.index("model_type")
        for (old, new) in MODEL_SUFFIX_REPLACE.items():
            for row_table in table:
                row_table[ip] = row_table[ip].replace(old, new)

    if "model_size" in show:
        ip = show.index("model_size")
        for row_table in table:
            row_table[ip] = float(row_table[ip]) / 1024 ** 3

    if "gpu_info" in show:
        ip = show.index("gpu_info")
        for row_table in table:
            for gns in GPU_NAME_STRIP:
                row_table[ip] = row_table[ip].replace(gns, "")

            gpu_names = row_table[ip].split(", ")
            num_gpus = len(gpu_names)
            all_names_the_same = len(set(gpu_names)) == 1
            if len(gpu_names) >= 2 and all_names_the_same:
                row_table[ip] = f"{num_gpus}x {gpu_names[0]}"

headers  = [pretty_names.get(p, p) for p in show]
if tool == "llama-bench":
    headers += ["Test", f"t/s {name_baseline}", f"t/s {name_compare}", "Speedup"]
elif tool == "test-backend-ops":
    headers += [f"{primary_metric} {name_baseline}", f"{primary_metric} {name_compare}", "Speedup"]
else:
    assert False

if known_args.plot:
    def create_performance_plot(table_data: list[list[str]], headers: list[str], baseline_name: str, compare_name: str, output_file: str, plot_x_param: str, log_scale: bool = False, tool_type: str = "llama-bench", metric_name: str = "t/s"):
        try:
            import matplotlib
            import matplotlib.pyplot as plt
            matplotlib.use('Agg')
        except ImportError as e:
            logger.error("matplotlib is required for --plot.")
            raise e

        data_headers = headers[:-4] # Exclude the last 4 columns (Test, baseline t/s, compare t/s, Speedup)
        plot_x_index = None
        plot_x_label = plot_x_param

        if plot_x_param not in ["n_prompt", "n_gen", "n_depth"]:
            pretty_name = LLAMA_BENCH_PRETTY_NAMES.get(plot_x_param, plot_x_param)
            if pretty_name in data_headers:
                plot_x_index = data_headers.index(pretty_name)
                plot_x_label = pretty_name
            elif plot_x_param in data_headers:
                plot_x_index = data_headers.index(plot_x_param)
                plot_x_label = plot_x_param
            else:
                logger.error(f"Parameter '{plot_x_param}' not found in current table columns. Available columns: {', '.join(data_headers)}")
                return

        grouped_data = {}

        for i, row in enumerate(table_data):
            group_key_parts = []
            test_name = row[-4]

            base_test = ""
            x_value = None

            if plot_x_param in ["n_prompt", "n_gen", "n_depth"]:
                for j, val in enumerate(row[:-4]):
                    header_name = data_headers[j]
                    if val is not None and str(val).strip():
                        group_key_parts.append(f"{header_name}={val}")

                if plot_x_param == "n_prompt" and "pp" in test_name:
                    base_test = test_name.split("@")[0]
                    x_value = base_test
                elif plot_x_param == "n_gen" and "tg" in test_name:
                    x_value = test_name.split("@")[0]
                elif plot_x_param == "n_depth" and "@d" in test_name:
                    base_test = test_name.split("@d")[0]
                    x_value = int(test_name.split("@d")[1])
                else:
                    base_test = test_name

                if base_test.strip():
                    group_key_parts.append(f"Test={base_test}")
            else:
                for j, val in enumerate(row[:-4]):
                    if j != plot_x_index:
                        header_name = data_headers[j]
                        if val is not None and str(val).strip():
                            group_key_parts.append(f"{header_name}={val}")
                    else:
                        x_value = val

                group_key_parts.append(f"Test={test_name}")

            group_key = tuple(group_key_parts)

            if group_key not in grouped_data:
                grouped_data[group_key] = []

            grouped_data[group_key].append({
                'x_value': x_value,
                'baseline': float(row[-3]),
                'compare': float(row[-2]),
                'speedup': float(row[-1])
            })

        if not grouped_data:
            logger.error("No data available for plotting")
            return

        def make_axes(num_groups, max_cols=2, base_size=(8, 4)):
            from math import ceil
            cols = 1 if num_groups == 1 else min(max_cols, num_groups)
            rows = ceil(num_groups / cols)

            # Scale figure size by grid dimensions
            w, h = base_size
            fig, ax_arr = plt.subplots(rows, cols,
                                       figsize=(w * cols, h * rows),
                                       squeeze=False)

            axes = ax_arr.flatten()[:num_groups]
            return fig, axes

        num_groups = len(grouped_data)
        fig, axes = make_axes(num_groups)

        plot_idx = 0

        for group_key, points in grouped_data.items():
            if plot_idx >= len(axes):
                break
            ax = axes[plot_idx]

            try:
                points_sorted = sorted(points, key=lambda p: float(p['x_value']) if p['x_value'] is not None else 0)
                x_values = [float(p['x_value']) if p['x_value'] is not None else 0 for p in points_sorted]
            except ValueError:
                points_sorted = sorted(points, key=lambda p: group_key)
                x_values = [p['x_value'] for p in points_sorted]

            baseline_vals = [p['baseline'] for p in points_sorted]
            compare_vals = [p['compare'] for p in points_sorted]

            ax.plot(x_values, baseline_vals, 'o-', color='skyblue',
                    label=f'{baseline_name}', linewidth=2, markersize=6)
            ax.plot(x_values, compare_vals, 's--', color='lightcoral', alpha=0.8,
                    label=f'{compare_name}', linewidth=2, markersize=6)

            if log_scale:
                ax.set_xscale('log', base=2)
                unique_x = sorted(set(x_values))
                ax.set_xticks(unique_x)
                ax.set_xticklabels([str(int(x)) for x in unique_x])

            title_parts = []
            for part in group_key:
                if '=' in part:
                    key, value = part.split('=', 1)
                    title_parts.append(f"{key}: {value}")

            title = ', '.join(title_parts) if title_parts else "Performance comparison"

            # Determine y-axis label based on tool type
            if tool_type == "llama-bench":
                y_label = "Tokens per second (t/s)"
            elif tool_type == "test-backend-ops":
                y_label = metric_name
            else:
                assert False

            ax.set_xlabel(plot_x_label, fontsize=12, fontweight='bold')
            ax.set_ylabel(y_label, fontsize=12, fontweight='bold')
            ax.set_title(title, fontsize=12, fontweight='bold')
            ax.legend(loc='best', fontsize=10)
            ax.grid(True, alpha=0.3)

            plot_idx += 1

        for i in range(plot_idx, len(axes)):
            axes[i].set_visible(False)

        fig.suptitle(f'Performance comparison: {compare_name} vs. {baseline_name}',
                     fontsize=14, fontweight='bold')
        fig.subplots_adjust(top=1)

        plt.tight_layout()
        plt.savefig(output_file, dpi=300, bbox_inches='tight')
        plt.close()

    create_performance_plot(table, headers, name_baseline, name_compare, known_args.plot, known_args.plot_x, known_args.plot_log_scale, tool, primary_metric)

print(tabulate( # noqa: NP100
    table,
    headers=headers,
    floatfmt=".2f",
    tablefmt=known_args.output
))