gh-138122: Implement PEP 799 (#138142)

Lib/profiling/sampling/sample.py

@@ -0,0 +1,864 @@
+import argparse
+import _remote_debugging
+import os
+import pstats
+import socket
+import subprocess
+import statistics
+import sys
+import sysconfig
+import time
+from collections import deque
+from _colorize import ANSIColors
+
+from .pstats_collector import PstatsCollector
+from .stack_collector import CollapsedStackCollector
+
+_FREE_THREADED_BUILD = sysconfig.get_config_var("Py_GIL_DISABLED") is not None
+_MAX_STARTUP_ATTEMPTS = 5
+_STARTUP_RETRY_DELAY_SECONDS = 0.1
+_HELP_DESCRIPTION = """Sample a process's stack frames and generate profiling data.
+Supports the following target modes:
+  - -p PID: Profile an existing process by PID
+  - -m MODULE [ARGS...]: Profile a module as python -m module ...
+  - filename [ARGS...]: Profile the specified script by running it in a subprocess
+
+Examples:
+  # Profile process 1234 for 10 seconds with default settings
+  python -m profiling.sampling -p 1234
+
+  # Profile a script by running it in a subprocess
+  python -m profiling.sampling myscript.py arg1 arg2
+
+  # Profile a module by running it as python -m module in a subprocess
+  python -m profiling.sampling -m mymodule arg1 arg2
+
+  # Profile with custom interval and duration, save to file
+  python -m profiling.sampling -i 50 -d 30 -o profile.stats -p 1234
+
+  # Generate collapsed stacks for flamegraph
+  python -m profiling.sampling --collapsed -p 1234
+
+  # Profile all threads, sort by total time
+  python -m profiling.sampling -a --sort-tottime -p 1234
+
+  # Profile for 1 minute with 1ms sampling interval
+  python -m profiling.sampling -i 1000 -d 60 -p 1234
+
+  # Show only top 20 functions sorted by direct samples
+  python -m profiling.sampling --sort-nsamples -l 20 -p 1234
+
+  # Profile all threads and save collapsed stacks
+  python -m profiling.sampling -a --collapsed -o stacks.txt -p 1234
+
+  # Profile with real-time sampling statistics
+  python -m profiling.sampling --realtime-stats -p 1234
+
+  # Sort by sample percentage to find most sampled functions
+  python -m profiling.sampling --sort-sample-pct -p 1234
+
+  # Sort by cumulative samples to find functions most on call stack
+  python -m profiling.sampling --sort-nsamples-cumul -p 1234"""
+
+
+# Constants for socket synchronization
+_SYNC_TIMEOUT = 5.0
+_PROCESS_KILL_TIMEOUT = 2.0
+_READY_MESSAGE = b"ready"
+_RECV_BUFFER_SIZE = 1024
+
+
+def _run_with_sync(original_cmd):
+    """Run a command with socket-based synchronization and return the process."""
+    # Create a TCP socket for synchronization with better socket options
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sync_sock:
+        # Set SO_REUSEADDR to avoid "Address already in use" errors
+        sync_sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+        sync_sock.bind(("127.0.0.1", 0))  # Let OS choose a free port
+        sync_port = sync_sock.getsockname()[1]
+        sync_sock.listen(1)
+        sync_sock.settimeout(_SYNC_TIMEOUT)
+
+        # Get current working directory to preserve it
+        cwd = os.getcwd()
+
+        # Build command using the sync coordinator
+        target_args = original_cmd[1:]  # Remove python executable
+        cmd = (sys.executable, "-m", "profiling.sampling._sync_coordinator", str(sync_port), cwd) + tuple(target_args)
+
+        # Start the process with coordinator
+        process = subprocess.Popen(cmd)
+
+        try:
+            # Wait for ready signal with timeout
+            with sync_sock.accept()[0] as conn:
+                ready_signal = conn.recv(_RECV_BUFFER_SIZE)
+
+                if ready_signal != _READY_MESSAGE:
+                    raise RuntimeError(f"Invalid ready signal received: {ready_signal!r}")
+
+        except socket.timeout:
+            # If we timeout, kill the process and raise an error
+            if process.poll() is None:
+                process.terminate()
+                try:
+                    process.wait(timeout=_PROCESS_KILL_TIMEOUT)
+                except subprocess.TimeoutExpired:
+                    process.kill()
+                    process.wait()
+            raise RuntimeError("Process failed to signal readiness within timeout")
+
+        return process
+
+
+
+
+class SampleProfiler:
+    def __init__(self, pid, sample_interval_usec, all_threads):
+        self.pid = pid
+        self.sample_interval_usec = sample_interval_usec
+        self.all_threads = all_threads
+        if _FREE_THREADED_BUILD:
+            self.unwinder = _remote_debugging.RemoteUnwinder(
+                self.pid, all_threads=self.all_threads
+            )
+        else:
+            only_active_threads = bool(self.all_threads)
+            self.unwinder = _remote_debugging.RemoteUnwinder(
+                self.pid, only_active_thread=only_active_threads
+            )
+        # Track sample intervals and total sample count
+        self.sample_intervals = deque(maxlen=100)
+        self.total_samples = 0
+        self.realtime_stats = False
+
+    def sample(self, collector, duration_sec=10):
+        sample_interval_sec = self.sample_interval_usec / 1_000_000
+        running_time = 0
+        num_samples = 0
+        errors = 0
+        start_time = next_time = time.perf_counter()
+        last_sample_time = start_time
+        realtime_update_interval = 1.0  # Update every second
+        last_realtime_update = start_time
+
+        while running_time < duration_sec:
+            current_time = time.perf_counter()
+            if next_time < current_time:
+                try:
+                    stack_frames = self.unwinder.get_stack_trace()
+                    collector.collect(stack_frames)
+                except ProcessLookupError:
+                    duration_sec = current_time - start_time
+                    break
+                except (RuntimeError, UnicodeDecodeError, MemoryError, OSError):
+                    errors += 1
+                except Exception as e:
+                    if not self._is_process_running():
+                        break
+                    raise e from None
+
+                # Track actual sampling intervals for real-time stats
+                if num_samples > 0:
+                    actual_interval = current_time - last_sample_time
+                    self.sample_intervals.append(
+                        1.0 / actual_interval
+                    )  # Convert to Hz
+                    self.total_samples += 1
+
+                    # Print real-time statistics if enabled
+                    if (
+                        self.realtime_stats
+                        and (current_time - last_realtime_update)
+                        >= realtime_update_interval
+                    ):
+                        self._print_realtime_stats()
+                        last_realtime_update = current_time
+
+                last_sample_time = current_time
+                num_samples += 1
+                next_time += sample_interval_sec
+
+            running_time = time.perf_counter() - start_time
+
+        # Clear real-time stats line if it was being displayed
+        if self.realtime_stats and len(self.sample_intervals) > 0:
+            print()  # Add newline after real-time stats
+
+        print(f"Captured {num_samples} samples in {running_time:.2f} seconds")
+        print(f"Sample rate: {num_samples / running_time:.2f} samples/sec")
+        print(f"Error rate: {(errors / num_samples) * 100:.2f}%")
+
+        expected_samples = int(duration_sec / sample_interval_sec)
+        if num_samples < expected_samples:
+            print(
+                f"Warning: missed {expected_samples - num_samples} samples "
+                f"from the expected total of {expected_samples} "
+                f"({(expected_samples - num_samples) / expected_samples * 100:.2f}%)"
+            )
+
+    def _is_process_running(self):
+        if sys.platform == "linux" or sys.platform == "darwin":
+            try:
+                os.kill(self.pid, 0)
+                return True
+            except ProcessLookupError:
+                return False
+        elif sys.platform == "win32":
+            try:
+                _remote_debugging.RemoteUnwinder(self.pid)
+            except Exception:
+                return False
+            return True
+        else:
+            raise ValueError(f"Unsupported platform: {sys.platform}")
+
+    def _print_realtime_stats(self):
+        """Print real-time sampling statistics."""
+        if len(self.sample_intervals) < 2:
+            return
+
+        # Calculate statistics on the Hz values (deque automatically maintains rolling window)
+        hz_values = list(self.sample_intervals)
+        mean_hz = statistics.mean(hz_values)
+        min_hz = min(hz_values)
+        max_hz = max(hz_values)
+
+        # Calculate microseconds per sample for all metrics (1/Hz * 1,000,000)
+        mean_us_per_sample = (1.0 / mean_hz) * 1_000_000 if mean_hz > 0 else 0
+        min_us_per_sample = (
+            (1.0 / max_hz) * 1_000_000 if max_hz > 0 else 0
+        )  # Min time = Max Hz
+        max_us_per_sample = (
+            (1.0 / min_hz) * 1_000_000 if min_hz > 0 else 0
+        )  # Max time = Min Hz
+
+        # Clear line and print stats
+        print(
+            f"\r\033[K{ANSIColors.BOLD_BLUE}Real-time sampling stats:{ANSIColors.RESET} "
+            f"{ANSIColors.YELLOW}Mean: {mean_hz:.1f}Hz ({mean_us_per_sample:.2f}µs){ANSIColors.RESET} "
+            f"{ANSIColors.GREEN}Min: {min_hz:.1f}Hz ({max_us_per_sample:.2f}µs){ANSIColors.RESET} "
+            f"{ANSIColors.RED}Max: {max_hz:.1f}Hz ({min_us_per_sample:.2f}µs){ANSIColors.RESET} "
+            f"{ANSIColors.CYAN}Samples: {self.total_samples}{ANSIColors.RESET}",
+            end="",
+            flush=True,
+        )
+
+
+def _determine_best_unit(max_value):
+    """Determine the best unit (s, ms, μs) and scale factor for a maximum value."""
+    if max_value >= 1.0:
+        return "s", 1.0
+    elif max_value >= 0.001:
+        return "ms", 1000.0
+    else:
+        return "μs", 1000000.0
+
+
+def print_sampled_stats(
+    stats, sort=-1, limit=None, show_summary=True, sample_interval_usec=100
+):
+    # Get the stats data
+    stats_list = []
+    for func, (
+        direct_calls,
+        cumulative_calls,
+        total_time,
+        cumulative_time,
+        callers,
+    ) in stats.stats.items():
+        stats_list.append(
+            (
+                func,
+                direct_calls,
+                cumulative_calls,
+                total_time,
+                cumulative_time,
+                callers,
+            )
+        )
+
+    # Calculate total samples for percentage calculations (using direct_calls)
+    total_samples = sum(
+        direct_calls for _, direct_calls, _, _, _, _ in stats_list
+    )
+
+    # Sort based on the requested field
+    sort_field = sort
+    if sort_field == -1:  # stdname
+        stats_list.sort(key=lambda x: str(x[0]))
+    elif sort_field == 0:  # nsamples (direct samples)
+        stats_list.sort(key=lambda x: x[1], reverse=True)  # direct_calls
+    elif sort_field == 1:  # tottime
+        stats_list.sort(key=lambda x: x[3], reverse=True)  # total_time
+    elif sort_field == 2:  # cumtime
+        stats_list.sort(key=lambda x: x[4], reverse=True)  # cumulative_time
+    elif sort_field == 3:  # sample%
+        stats_list.sort(
+            key=lambda x: (x[1] / total_samples * 100)
+            if total_samples > 0
+            else 0,
+            reverse=True,  # direct_calls percentage
+        )
+    elif sort_field == 4:  # cumul%
+        stats_list.sort(
+            key=lambda x: (x[2] / total_samples * 100)
+            if total_samples > 0
+            else 0,
+            reverse=True,  # cumulative_calls percentage
+        )
+    elif sort_field == 5:  # nsamples (cumulative samples)
+        stats_list.sort(key=lambda x: x[2], reverse=True)  # cumulative_calls
+
+    # Apply limit if specified
+    if limit is not None:
+        stats_list = stats_list[:limit]
+
+    # Determine the best unit for time columns based on maximum values
+    max_total_time = max(
+        (total_time for _, _, _, total_time, _, _ in stats_list), default=0
+    )
+    max_cumulative_time = max(
+        (cumulative_time for _, _, _, _, cumulative_time, _ in stats_list),
+        default=0,
+    )
+
+    total_time_unit, total_time_scale = _determine_best_unit(max_total_time)
+    cumulative_time_unit, cumulative_time_scale = _determine_best_unit(
+        max_cumulative_time
+    )
+
+    # Define column widths for consistent alignment
+    col_widths = {
+        "nsamples": 15,  # "nsamples" column (inline/cumulative format)
+        "sample_pct": 8,  # "sample%" column
+        "tottime": max(12, len(f"tottime ({total_time_unit})")),
+        "cum_pct": 8,  # "cumul%" column
+        "cumtime": max(12, len(f"cumtime ({cumulative_time_unit})")),
+    }
+
+    # Print header with colors and proper alignment
+    print(f"{ANSIColors.BOLD_BLUE}Profile Stats:{ANSIColors.RESET}")
+
+    header_nsamples = f"{ANSIColors.BOLD_BLUE}{'nsamples':>{col_widths['nsamples']}}{ANSIColors.RESET}"
+    header_sample_pct = f"{ANSIColors.BOLD_BLUE}{'sample%':>{col_widths['sample_pct']}}{ANSIColors.RESET}"
+    header_tottime = f"{ANSIColors.BOLD_BLUE}{f'tottime ({total_time_unit})':>{col_widths['tottime']}}{ANSIColors.RESET}"
+    header_cum_pct = f"{ANSIColors.BOLD_BLUE}{'cumul%':>{col_widths['cum_pct']}}{ANSIColors.RESET}"
+    header_cumtime = f"{ANSIColors.BOLD_BLUE}{f'cumtime ({cumulative_time_unit})':>{col_widths['cumtime']}}{ANSIColors.RESET}"
+    header_filename = (
+        f"{ANSIColors.BOLD_BLUE}filename:lineno(function){ANSIColors.RESET}"
+    )
+
+    print(
+        f"{header_nsamples}  {header_sample_pct}  {header_tottime}  {header_cum_pct}  {header_cumtime}  {header_filename}"
+    )
+
+    # Print each line with proper alignment
+    for (
+        func,
+        direct_calls,
+        cumulative_calls,
+        total_time,
+        cumulative_time,
+        callers,
+    ) in stats_list:
+        # Calculate percentages
+        sample_pct = (
+            (direct_calls / total_samples * 100) if total_samples > 0 else 0
+        )
+        cum_pct = (
+            (cumulative_calls / total_samples * 100)
+            if total_samples > 0
+            else 0
+        )
+
+        # Format values with proper alignment - always use A/B format
+        nsamples_str = f"{direct_calls}/{cumulative_calls}"
+        nsamples_str = f"{nsamples_str:>{col_widths['nsamples']}}"
+        sample_pct_str = f"{sample_pct:{col_widths['sample_pct']}.1f}"
+        tottime = f"{total_time * total_time_scale:{col_widths['tottime']}.3f}"
+        cum_pct_str = f"{cum_pct:{col_widths['cum_pct']}.1f}"
+        cumtime = f"{cumulative_time * cumulative_time_scale:{col_widths['cumtime']}.3f}"
+
+        # Format the function name with colors
+        func_name = (
+            f"{ANSIColors.GREEN}{func[0]}{ANSIColors.RESET}:"
+            f"{ANSIColors.YELLOW}{func[1]}{ANSIColors.RESET}("
+            f"{ANSIColors.CYAN}{func[2]}{ANSIColors.RESET})"
+        )
+
+        # Print the formatted line with consistent spacing
+        print(
+            f"{nsamples_str}  {sample_pct_str}  {tottime}  {cum_pct_str}  {cumtime}  {func_name}"
+        )
+
+    # Print legend
+    print(f"\n{ANSIColors.BOLD_BLUE}Legend:{ANSIColors.RESET}")
+    print(
+        f"  {ANSIColors.YELLOW}nsamples{ANSIColors.RESET}: Direct/Cumulative samples (direct executing / on call stack)"
+    )
+    print(
+        f"  {ANSIColors.YELLOW}sample%{ANSIColors.RESET}: Percentage of total samples this function was directly executing"
+    )
+    print(
+        f"  {ANSIColors.YELLOW}tottime{ANSIColors.RESET}: Estimated total time spent directly in this function"
+    )
+    print(
+        f"  {ANSIColors.YELLOW}cumul%{ANSIColors.RESET}: Percentage of total samples when this function was on the call stack"
+    )
+    print(
+        f"  {ANSIColors.YELLOW}cumtime{ANSIColors.RESET}: Estimated cumulative time (including time in called functions)"
+    )
+    print(
+        f"  {ANSIColors.YELLOW}filename:lineno(function){ANSIColors.RESET}: Function location and name"
+    )
+
+    def _format_func_name(func):
+        """Format function name with colors."""
+        return (
+            f"{ANSIColors.GREEN}{func[0]}{ANSIColors.RESET}:"
+            f"{ANSIColors.YELLOW}{func[1]}{ANSIColors.RESET}("
+            f"{ANSIColors.CYAN}{func[2]}{ANSIColors.RESET})"
+        )
+
+    def _print_top_functions(stats_list, title, key_func, format_line, n=3):
+        """Print top N functions sorted by key_func with formatted output."""
+        print(f"\n{ANSIColors.BOLD_BLUE}{title}:{ANSIColors.RESET}")
+        sorted_stats = sorted(stats_list, key=key_func, reverse=True)
+        for stat in sorted_stats[:n]:
+            if line := format_line(stat):
+                print(f"  {line}")
+
+    # Print summary of interesting functions if enabled
+    if show_summary and stats_list:
+        print(
+            f"\n{ANSIColors.BOLD_BLUE}Summary of Interesting Functions:{ANSIColors.RESET}"
+        )
+
+        # Aggregate stats by fully qualified function name (ignoring line numbers)
+        func_aggregated = {}
+        for (
+            func,
+            direct_calls,
+            cumulative_calls,
+            total_time,
+            cumulative_time,
+            callers,
+        ) in stats_list:
+            # Use filename:function_name as the key to get fully qualified name
+            qualified_name = f"{func[0]}:{func[2]}"
+            if qualified_name not in func_aggregated:
+                func_aggregated[qualified_name] = [
+                    0,
+                    0,
+                    0,
+                    0,
+                ]  # direct_calls, cumulative_calls, total_time, cumulative_time
+            func_aggregated[qualified_name][0] += direct_calls
+            func_aggregated[qualified_name][1] += cumulative_calls
+            func_aggregated[qualified_name][2] += total_time
+            func_aggregated[qualified_name][3] += cumulative_time
+
+        # Convert aggregated data back to list format for processing
+        aggregated_stats = []
+        for qualified_name, (
+            prim_calls,
+            total_calls,
+            total_time,
+            cumulative_time,
+        ) in func_aggregated.items():
+            # Parse the qualified name back to filename and function name
+            if ":" in qualified_name:
+                filename, func_name = qualified_name.rsplit(":", 1)
+            else:
+                filename, func_name = "", qualified_name
+            # Create a dummy func tuple with filename and function name for display
+            dummy_func = (filename, "", func_name)
+            aggregated_stats.append(
+                (
+                    dummy_func,
+                    prim_calls,
+                    total_calls,
+                    total_time,
+                    cumulative_time,
+                    {},
+                )
+            )
+
+        # Determine best units for summary metrics
+        max_total_time = max(
+            (total_time for _, _, _, total_time, _, _ in aggregated_stats),
+            default=0,
+        )
+        max_cumulative_time = max(
+            (
+                cumulative_time
+                for _, _, _, _, cumulative_time, _ in aggregated_stats
+            ),
+            default=0,
+        )
+
+        total_unit, total_scale = _determine_best_unit(max_total_time)
+        cumulative_unit, cumulative_scale = _determine_best_unit(
+            max_cumulative_time
+        )
+
+        # Functions with highest direct/cumulative ratio (hot spots)
+        def format_hotspots(stat):
+            func, direct_calls, cumulative_calls, total_time, _, _ = stat
+            if direct_calls > 0 and cumulative_calls > 0:
+                ratio = direct_calls / cumulative_calls
+                direct_pct = (
+                    (direct_calls / total_samples * 100)
+                    if total_samples > 0
+                    else 0
+                )
+                return (
+                    f"{ratio:.3f} direct/cumulative ratio, "
+                    f"{direct_pct:.1f}% direct samples: {_format_func_name(func)}"
+                )
+            return None
+
+        _print_top_functions(
+            aggregated_stats,
+            "Functions with Highest Direct/Cumulative Ratio (Hot Spots)",
+            key_func=lambda x: (x[1] / x[2]) if x[2] > 0 else 0,
+            format_line=format_hotspots,
+        )
+
+        # Functions with highest call frequency (cumulative/direct difference)
+        def format_call_frequency(stat):
+            func, direct_calls, cumulative_calls, total_time, _, _ = stat
+            if cumulative_calls > direct_calls:
+                call_frequency = cumulative_calls - direct_calls
+                cum_pct = (
+                    (cumulative_calls / total_samples * 100)
+                    if total_samples > 0
+                    else 0
+                )
+                return (
+                    f"{call_frequency:d} indirect calls, "
+                    f"{cum_pct:.1f}% total stack presence: {_format_func_name(func)}"
+                )
+            return None
+
+        _print_top_functions(
+            aggregated_stats,
+            "Functions with Highest Call Frequency (Indirect Calls)",
+            key_func=lambda x: x[2] - x[1],  # Sort by (cumulative - direct)
+            format_line=format_call_frequency,
+        )
+
+        # Functions with highest cumulative-to-direct multiplier (call magnification)
+        def format_call_magnification(stat):
+            func, direct_calls, cumulative_calls, total_time, _, _ = stat
+            if direct_calls > 0 and cumulative_calls > direct_calls:
+                multiplier = cumulative_calls / direct_calls
+                indirect_calls = cumulative_calls - direct_calls
+                return (
+                    f"{multiplier:.1f}x call magnification, "
+                    f"{indirect_calls:d} indirect calls from {direct_calls:d} direct: {_format_func_name(func)}"
+                )
+            return None
+
+        _print_top_functions(
+            aggregated_stats,
+            "Functions with Highest Call Magnification (Cumulative/Direct)",
+            key_func=lambda x: (x[2] / x[1])
+            if x[1] > 0
+            else 0,  # Sort by cumulative/direct ratio
+            format_line=format_call_magnification,
+        )
+
+
+def sample(
+    pid,
+    *,
+    sort=2,
+    sample_interval_usec=100,
+    duration_sec=10,
+    filename=None,
+    all_threads=False,
+    limit=None,
+    show_summary=True,
+    output_format="pstats",
+    realtime_stats=False,
+):
+    profiler = SampleProfiler(
+        pid, sample_interval_usec, all_threads=all_threads
+    )
+    profiler.realtime_stats = realtime_stats
+
+    collector = None
+    match output_format:
+        case "pstats":
+            collector = PstatsCollector(sample_interval_usec)
+        case "collapsed":
+            collector = CollapsedStackCollector()
+            filename = filename or f"collapsed.{pid}.txt"
+        case _:
+            raise ValueError(f"Invalid output format: {output_format}")
+
+    profiler.sample(collector, duration_sec)
+
+    if output_format == "pstats" and not filename:
+        stats = pstats.SampledStats(collector).strip_dirs()
+        print_sampled_stats(
+            stats, sort, limit, show_summary, sample_interval_usec
+        )
+    else:
+        collector.export(filename)
+
+
+def _validate_collapsed_format_args(args, parser):
+    # Check for incompatible pstats options
+    invalid_opts = []
+
+    # Get list of pstats-specific options
+    pstats_options = {"sort": None, "limit": None, "no_summary": False}
+
+    # Find the default values from the argument definitions
+    for action in parser._actions:
+        if action.dest in pstats_options and hasattr(action, "default"):
+            pstats_options[action.dest] = action.default
+
+    # Check if any pstats-specific options were provided by comparing with defaults
+    for opt, default in pstats_options.items():
+        if getattr(args, opt) != default:
+            invalid_opts.append(opt.replace("no_", ""))
+
+    if invalid_opts:
+        parser.error(
+            f"The following options are only valid with --pstats format: {', '.join(invalid_opts)}"
+        )
+
+    # Set default output filename for collapsed format only if we have a PID
+    # For module/script execution, this will be set later with the subprocess PID
+    if not args.outfile and args.pid is not None:
+        args.outfile = f"collapsed.{args.pid}.txt"
+
+
+def wait_for_process_and_sample(pid, sort_value, args):
+    """Sample the process immediately since it has already signaled readiness."""
+    # Set default collapsed filename with subprocess PID if not already set
+    filename = args.outfile
+    if not filename and args.format == "collapsed":
+        filename = f"collapsed.{pid}.txt"
+
+    sample(
+        pid,
+        sort=sort_value,
+        sample_interval_usec=args.interval,
+        duration_sec=args.duration,
+        filename=filename,
+        all_threads=args.all_threads,
+        limit=args.limit,
+        show_summary=not args.no_summary,
+        output_format=args.format,
+        realtime_stats=args.realtime_stats,
+    )
+
+
+def main():
+    # Create the main parser
+    parser = argparse.ArgumentParser(
+        description=_HELP_DESCRIPTION,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+
+    # Target selection
+    target_group = parser.add_mutually_exclusive_group(required=False)
+    target_group.add_argument(
+        "-p", "--pid", type=int, help="Process ID to sample"
+    )
+    target_group.add_argument(
+        "-m", "--module",
+        help="Run and profile a module as python -m module [ARGS...]"
+    )
+    parser.add_argument(
+        "args",
+        nargs=argparse.REMAINDER,
+        help="Script to run and profile, with optional arguments"
+    )
+
+    # Sampling options
+    sampling_group = parser.add_argument_group("Sampling configuration")
+    sampling_group.add_argument(
+        "-i",
+        "--interval",
+        type=int,
+        default=100,
+        help="Sampling interval in microseconds (default: 100)",
+    )
+    sampling_group.add_argument(
+        "-d",
+        "--duration",
+        type=int,
+        default=10,
+        help="Sampling duration in seconds (default: 10)",
+    )
+    sampling_group.add_argument(
+        "-a",
+        "--all-threads",
+        action="store_true",
+        help="Sample all threads in the process instead of just the main thread",
+    )
+    sampling_group.add_argument(
+        "--realtime-stats",
+        action="store_true",
+        default=False,
+        help="Print real-time sampling statistics (Hz, mean, min, max, stdev) during profiling",
+    )
+
+    # Output format selection
+    output_group = parser.add_argument_group("Output options")
+    output_format = output_group.add_mutually_exclusive_group()
+    output_format.add_argument(
+        "--pstats",
+        action="store_const",
+        const="pstats",
+        dest="format",
+        default="pstats",
+        help="Generate pstats output (default)",
+    )
+    output_format.add_argument(
+        "--collapsed",
+        action="store_const",
+        const="collapsed",
+        dest="format",
+        help="Generate collapsed stack traces for flamegraphs",
+    )
+
+    output_group.add_argument(
+        "-o",
+        "--outfile",
+        help="Save output to a file (if omitted, prints to stdout for pstats, "
+        "or saves to collapsed.<pid>.txt for collapsed format)",
+    )
+
+    # pstats-specific options
+    pstats_group = parser.add_argument_group("pstats format options")
+    sort_group = pstats_group.add_mutually_exclusive_group()
+    sort_group.add_argument(
+        "--sort-nsamples",
+        action="store_const",
+        const=0,
+        dest="sort",
+        help="Sort by number of direct samples (nsamples column)",
+    )
+    sort_group.add_argument(
+        "--sort-tottime",
+        action="store_const",
+        const=1,
+        dest="sort",
+        help="Sort by total time (tottime column)",
+    )
+    sort_group.add_argument(
+        "--sort-cumtime",
+        action="store_const",
+        const=2,
+        dest="sort",
+        help="Sort by cumulative time (cumtime column, default)",
+    )
+    sort_group.add_argument(
+        "--sort-sample-pct",
+        action="store_const",
+        const=3,
+        dest="sort",
+        help="Sort by sample percentage (sample%% column)",
+    )
+    sort_group.add_argument(
+        "--sort-cumul-pct",
+        action="store_const",
+        const=4,
+        dest="sort",
+        help="Sort by cumulative sample percentage (cumul%% column)",
+    )
+    sort_group.add_argument(
+        "--sort-nsamples-cumul",
+        action="store_const",
+        const=5,
+        dest="sort",
+        help="Sort by cumulative samples (nsamples column, cumulative part)",
+    )
+    sort_group.add_argument(
+        "--sort-name",
+        action="store_const",
+        const=-1,
+        dest="sort",
+        help="Sort by function name",
+    )
+
+    pstats_group.add_argument(
+        "-l",
+        "--limit",
+        type=int,
+        help="Limit the number of rows in the output",
+        default=15,
+    )
+    pstats_group.add_argument(
+        "--no-summary",
+        action="store_true",
+        help="Disable the summary section in the output",
+    )
+
+    args = parser.parse_args()
+
+    # Validate format-specific arguments
+    if args.format == "collapsed":
+        _validate_collapsed_format_args(args, parser)
+
+    sort_value = args.sort if args.sort is not None else 2
+
+    if args.module is not None and not args.module:
+        parser.error("argument -m/--module: expected one argument")
+
+    # Validate that we have exactly one target type
+    # Note: args can be present with -m (module arguments) but not as standalone script
+    has_pid = args.pid is not None
+    has_module = args.module is not None
+    has_script = bool(args.args) and args.module is None
+
+    target_count = sum([has_pid, has_module, has_script])
+
+    if target_count == 0:
+        parser.error("one of the arguments -p/--pid -m/--module or script name is required")
+    elif target_count > 1:
+        parser.error("only one target type can be specified: -p/--pid, -m/--module, or script")
+
+    if args.pid:
+        sample(
+            args.pid,
+            sample_interval_usec=args.interval,
+            duration_sec=args.duration,
+            filename=args.outfile,
+            all_threads=args.all_threads,
+            limit=args.limit,
+            sort=sort_value,
+            show_summary=not args.no_summary,
+            output_format=args.format,
+            realtime_stats=args.realtime_stats,
+        )
+    elif args.module or args.args:
+        if args.module:
+            cmd = (sys.executable, "-m", args.module, *args.args)
+        else:
+            cmd = (sys.executable, *args.args)
+
+        # Use synchronized process startup
+        process = _run_with_sync(cmd)
+
+        # Process has already signaled readiness, start sampling immediately
+        try:
+            wait_for_process_and_sample(process.pid, sort_value, args)
+        finally:
+            if process.poll() is None:
+                process.terminate()
+                try:
+                    process.wait(timeout=2)
+                except subprocess.TimeoutExpired:
+                    process.kill()
+                    process.wait()
+
+if __name__ == "__main__":
+    main()