Upload lib/python3.12/site-packages/triton/testing.py with huggingface_hub

lib/python3.12/site-packages/triton/testing.py

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+import functools
+import os
+import subprocess
+import sys
+from contextlib import contextmanager
+from typing import Any, Dict, List
+from . import language as tl
+
+
+def nvsmi(attrs):
+    attrs = ','.join(attrs)
+    cmd = ['nvidia-smi', '-i', '0', '--query-gpu=' + attrs, '--format=csv,noheader,nounits']
+    out = subprocess.check_output(cmd)
+    ret = out.decode(sys.stdout.encoding).split(',')
+    ret = [int(x) for x in ret]
+    return ret
+
+
+def do_bench_cudagraph(fn, rep=20, grad_to_none=None, return_mode="mean"):
+    """
+    Benchmark the runtime of the provided function.
+
+    :param fn: Function to benchmark
+    :type fn: Callable
+    :param rep: Repetition time (in ms)
+    :type rep: int
+    :param grad_to_none: Reset the gradient of the provided tensor to None
+    :type grad_to_none: torch.tensor, optional
+    """
+    import torch
+    assert return_mode in ["min", "max", "mean", "median"]
+
+    if torch.cuda.current_stream() == torch.cuda.default_stream():
+        raise RuntimeError("Cannot capture graph in default stream. Please use side stream in benchmark code.")
+    # warmup
+    fn()
+    # step 1 - we estimate the amount of time the kernel call takes
+    # NOTE: this estimate isn't super accurate because the GPU isn't warmed up at this point
+    #       but it is probably good enough
+    if grad_to_none is not None:
+        for x in grad_to_none:
+            x.detach_()
+            x.requires_grad_(True)
+            x.grad = None
+    g = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(g):
+        fn()
+    torch.cuda.synchronize()
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
+    start_event.record()
+    g.replay()
+    end_event.record()
+    torch.cuda.synchronize()
+    estimate_ms = start_event.elapsed_time(end_event)
+    n_repeat = max(1, int(rep / estimate_ms))
+    # step 2 - construct a cuda graph with `n_repeat` unrolled function calls to minimize
+    # host overhead
+    g = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(g):
+        for i in range(n_repeat):
+            if grad_to_none is not None:
+                for x in grad_to_none:
+                    x.grad = None
+            fn()
+    torch.cuda.synchronize()
+    # measure time and return
+    ret = []
+    n_retries = 10
+    for i in range(n_retries):
+        start_event = torch.cuda.Event(enable_timing=True)
+        end_event = torch.cuda.Event(enable_timing=True)
+        start_event.record()
+        g.replay()
+        end_event.record()
+        torch.cuda.synchronize()
+        ret += [start_event.elapsed_time(end_event) / n_repeat]
+    times = torch.tensor(ret)
+    return getattr(torch, return_mode)(times).item()
+
+
+def do_bench(fn, warmup=25, rep=100, grad_to_none=None, quantiles=None, fast_flush=True, return_mode="mean",
+             device_type="cuda"):
+    """
+    Benchmark the runtime of the provided function. By default, return the median runtime of :code:`fn` along with
+    the 20-th and 80-th performance percentile.
+
+    :param fn: Function to benchmark
+    :type fn: Callable
+    :param warmup: Warmup time (in ms)
+    :type warmup: int
+    :param rep: Repetition time (in ms)
+    :type rep: int
+    :param grad_to_none: Reset the gradient of the provided tensor to None
+    :type grad_to_none: torch.tensor, optional
+    :param quantiles: Performance percentile to return in addition to the median.
+    :type quantiles: list[float]
+    :param fast_flush: Use faster kernel to flush L2 between measurements
+    :type fast_flush: bool
+    """
+    assert return_mode in ["min", "max", "mean", "median"]
+    import torch
+
+    di = torch._dynamo.device_interface.get_interface_for_device(device_type)
+
+    fn()
+    di.synchronize()
+
+    # We maintain a buffer of 256 MB that we clear
+    # before each kernel call to make sure that the L2
+    # doesn't contain any input data before the run
+    if fast_flush:
+        cache = torch.empty(int(256e6 // 4), dtype=torch.int, device=device_type)
+    else:
+        cache = torch.empty(int(256e6), dtype=torch.int8, device=device_type)
+
+    # Estimate the runtime of the function
+    start_event = di.Event(enable_timing=True)
+    end_event = di.Event(enable_timing=True)
+    start_event.record()
+    for _ in range(5):
+        cache.zero_()
+        fn()
+    end_event.record()
+    di.synchronize()
+    estimate_ms = start_event.elapsed_time(end_event) / 5
+
+    # compute number of warmup and repeat
+    n_warmup = max(1, int(warmup / estimate_ms))
+    n_repeat = max(1, int(rep / estimate_ms))
+    start_event = [di.Event(enable_timing=True) for i in range(n_repeat)]
+    end_event = [di.Event(enable_timing=True) for i in range(n_repeat)]
+    # Warm-up
+    for _ in range(n_warmup):
+        fn()
+    # Benchmark
+    for i in range(n_repeat):
+        # we don't want `fn` to accumulate gradient values
+        # if it contains a backward pass. So we clear the
+        # provided gradients
+        if grad_to_none is not None:
+            for x in grad_to_none:
+                x.grad = None
+        # we clear the L2 cache before each run
+        cache.zero_()
+        # record time of `fn`
+        start_event[i].record()
+        fn()
+        end_event[i].record()
+    # Record clocks
+    di.synchronize()
+    times = torch.tensor([s.elapsed_time(e) for s, e in zip(start_event, end_event)], dtype=torch.float)
+    if quantiles is not None:
+        ret = torch.quantile(times, torch.tensor(quantiles, dtype=torch.float)).tolist()
+        if len(ret) == 1:
+            ret = ret[0]
+        return ret
+    return getattr(torch, return_mode)(times).item()
+
+
+def assert_close(x, y, atol=None, rtol=None, err_msg=''):
+    import numpy as np
+    import torch
+
+    # canonicalize arguments to be tensors
+    if not isinstance(x, torch.Tensor):
+        x = torch.tensor(x)
+    if not isinstance(y, torch.Tensor):
+        y = torch.tensor(y)
+    # absolute tolerance
+    if atol is None:
+        atol = 1e-2
+    atol = atol(x.dtype) if callable(atol) else atol
+    # relative tolerance hook
+    if rtol is None:
+        rtol = 0.
+    rtol = rtol(x.dtype) if callable(rtol) else rtol
+    # we use numpy instead of pytorch
+    # as it seems more memory efficient
+    # pytorch tends to oom on large tensors
+    if isinstance(x, torch.Tensor):
+        if x.dtype == torch.bfloat16:
+            x = x.float()
+        x = x.cpu().detach().numpy()
+    if isinstance(y, torch.Tensor):
+        if y.dtype == torch.bfloat16:
+            y = y.float()
+        y = y.cpu().detach().numpy()
+    # we handle size==1 case separately as we can
+    # provide better error message there
+    if x.size > 1 or y.size > 1:
+        np.testing.assert_allclose(x, y, atol=atol, rtol=rtol, equal_nan=True)
+        return
+    if not np.allclose(x, y, atol=atol, rtol=rtol):
+        raise AssertionError(f'{err_msg} {x} is not close to {y} (atol={atol}, rtol={rtol})')
+
+
+class Benchmark:
+    """
+    This class is used by the :code:`perf_report` function to generate line plots with a concise API.
+    """
+
+    def __init__(
+        self,
+        x_names: List[str],
+        x_vals: List[Any],
+        line_arg: str,
+        line_vals: List[Any],
+        line_names: List[str],
+        plot_name: str,
+        args: Dict[str, Any],
+        xlabel: str = '',
+        ylabel: str = '',
+        x_log: bool = False,
+        y_log: bool = False,
+        color=None,
+        styles=None,
+    ):
+        """
+        Constructor.
+        x_vals can be a list of scalars or a list of tuples/lists. If x_vals is a list
+        of scalars and there are multiple x_names, all arguments will have the same value.
+        If x_vals is a list of tuples/lists, each element should have the same length as
+        x_names.
+
+        :param x_names: Name of the arguments that should appear on the x axis of the plot.
+        :type x_names: List[str]
+        :param x_vals: List of values to use for the arguments in :code:`x_names`.
+        :type x_vals: List[Any]
+        :param line_arg: Argument name for which different values correspond to different lines in the plot.
+        :type line_arg: str
+        :param line_vals: List of values to use for the arguments in :code:`line_arg`.
+        :type line_vals: List[Any]
+        :param line_names: Label names for the different lines.
+        :type line_names: List[str]
+        :param plot_name: Name of the plot.
+        :type plot_name: str
+        :param args: Dictionary of keyword arguments to remain fixed throughout the benchmark.
+        :type args: Dict[str, Any]
+        :param xlabel: Label for the x axis of the plot.
+        :type xlabel: str, optional
+        :param ylabel: Label for the y axis of the plot.
+        :type ylabel: str, optional
+        :param x_log: Whether the x axis should be log scale.
+        :type x_log: bool, optional
+        :param y_log: Whether the y axis should be log scale.
+        :type y_log: bool, optional
+        """
+        self.x_names = x_names
+        self.x_vals = x_vals
+        self.x_log = x_log
+        self.line_arg = line_arg
+        self.line_vals = line_vals
+        self.line_names = line_names
+        self.y_log = y_log
+        self.styles = styles
+        # plot info
+        self.xlabel = xlabel
+        self.ylabel = ylabel
+        self.plot_name = plot_name
+        self.args = args
+
+
+class Mark:
+
+    def __init__(self, fn, benchmarks):
+        self.fn = fn
+        self.benchmarks = benchmarks
+
+    def _run(self, bench: Benchmark, save_path: str, show_plots: bool, print_data: bool, diff_col=False,
+             save_precision=6, **kwrags):
+        import os
+
+        import matplotlib.pyplot as plt
+        import pandas as pd
+        y_mean = bench.line_names
+        y_min = [f'{x}-min' for x in bench.line_names]
+        y_max = [f'{x}-max' for x in bench.line_names]
+        x_names = list(bench.x_names)
+        df = pd.DataFrame(columns=x_names + y_mean + y_min + y_max)
+        for x in bench.x_vals:
+            # x can be a single value or a sequence of values.
+            if not isinstance(x, (list, tuple)):
+                x = [x for _ in x_names]
+
+            if len(x) != len(x_names):
+                raise ValueError(f"Expected {len(x_names)} values, got {x}")
+            x_args = dict(zip(x_names, x))
+
+            row_mean, row_min, row_max = [], [], []
+            for y in bench.line_vals:
+                ret = self.fn(**x_args, **{bench.line_arg: y}, **bench.args, **kwrags)
+                try:
+                    y_mean, y_min, y_max = ret
+                except TypeError:
+                    y_mean, y_min, y_max = ret, None, None
+                row_mean += [y_mean]
+                row_min += [y_min]
+                row_max += [y_max]
+            df.loc[len(df)] = list(x) + row_mean + row_min + row_max
+
+        if bench.plot_name:
+            plt.figure()
+            ax = plt.subplot()
+            # Plot first x value on x axis if there are multiple.
+            first_x = x_names[0]
+            for i, y in enumerate(bench.line_names):
+                y_min, y_max = df[y + '-min'], df[y + '-max']
+                col = bench.styles[i][0] if bench.styles else None
+                sty = bench.styles[i][1] if bench.styles else None
+                ax.plot(df[first_x], df[y], label=y, color=col, ls=sty)
+                if not y_min.isnull().all() and not y_max.isnull().all():
+                    y_min = y_min.astype(float)
+                    y_max = y_max.astype(float)
+                    ax.fill_between(df[first_x], y_min, y_max, alpha=0.15, color=col)
+            ax.legend()
+            ax.set_xlabel(bench.xlabel or first_x)
+            ax.set_ylabel(bench.ylabel)
+            # ax.set_title(bench.plot_name)
+            ax.set_xscale("log" if bench.x_log else "linear")
+            ax.set_yscale("log" if bench.y_log else "linear")
+            if show_plots:
+                plt.show()
+            if save_path:
+                plt.savefig(os.path.join(save_path, f"{bench.plot_name}.png"))
+        df = df[x_names + bench.line_names]
+        if diff_col and df.shape[1] == 2:
+            col0, col1 = df.columns.tolist()
+            df['Diff'] = df[col1] - df[col0]
+
+        if print_data:
+            print(bench.plot_name + ':')
+            print(df.to_string())
+        if save_path:
+            df.to_csv(os.path.join(save_path, f"{bench.plot_name}.csv"), float_format=f"%.{save_precision}f",
+                      index=False)
+        return df
+
+    def run(self, show_plots=False, print_data=False, save_path='', return_df=False, **kwargs):
+        has_single_bench = isinstance(self.benchmarks, Benchmark)
+        benchmarks = [self.benchmarks] if has_single_bench else self.benchmarks
+        result_dfs = []
+        if save_path:
+            # Create directory if it doesn't exist
+            os.makedirs(save_path, exist_ok=True)
+            html = open(os.path.join(save_path, "results.html"), "w")
+            html.write("<html><body>\n")
+        for bench in benchmarks:
+            result_dfs.append(self._run(bench, save_path, show_plots, print_data, **kwargs))
+            if save_path:
+                html.write(f"<image src=\"{bench.plot_name}.png\"/>\n")
+        if save_path:
+            html.write("</body></html>\n")
+            html.close()
+        if return_df:
+            if has_single_bench:
+                return result_dfs[0]
+            else:
+                return result_dfs
+        return None
+
+
+def perf_report(benchmarks):
+    """
+    Mark a function for benchmarking. The benchmark can then be executed by using the :code:`.run` method on the return value.
+
+    :param benchmarks: Benchmarking configurations.
+    :type benchmarks: List of :class:`Benchmark`
+    """
+    wrapper = lambda fn: Mark(fn, benchmarks)
+    return wrapper
+
+
+def get_dram_gbps(device=None):
+    ''' return DRAM bandwidth in GB/s '''
+    import torch
+
+    from .runtime import driver
+    if not device:
+        device = torch.cuda.current_device()
+    mem_clock_khz = driver.active.utils.get_device_properties(device)["mem_clock_rate"]  # in kHz
+    bus_width = driver.active.utils.get_device_properties(device)["mem_bus_width"]
+    bw_gbps = mem_clock_khz * bus_width * 2 / 1e6 / 8  # In GB/s
+    return bw_gbps
+
+
+def get_max_tensorcore_tflops(dtype, clock_rate, device=None):
+    import torch
+
+    from .runtime import driver
+    if not device:
+        device = torch.cuda.current_device()
+
+    num_subcores = driver.active.utils.get_device_properties(device)["multiprocessor_count"] * 4
+    capability = torch.cuda.get_device_capability(device)
+    if capability[0] < 8:
+        assert dtype == torch.float16
+        ops_per_sub_core = 256  # 2 4x4x4 Tensor Cores
+    else:
+        if dtype in [torch.float32, torch.int32]:
+            ops_per_sub_core = 256
+        elif dtype in [torch.float16, torch.bfloat16, torch.int16]:
+            ops_per_sub_core = 512
+        elif dtype in [torch.int8, tl.float8e4nv, tl.float8e4b15, tl.float8e5]:
+            ops_per_sub_core = 1024
+        else:
+            raise RuntimeError("dtype not supported")
+    tflops = num_subcores * clock_rate * ops_per_sub_core * 1e-9
+    return tflops
+
+
+# create decorator that wraps test function into
+# a cuda-memcheck system call
+
+
+def cuda_memcheck(**target_kwargs):
+
+    def decorator(test_fn):
+
+        @functools.wraps(test_fn)
+        def wrapper(*args, **kwargs):
+            import psutil
+            ppid_name = psutil.Process(os.getppid()).name()
+            run_cuda_memcheck = target_kwargs.items() <= kwargs.items()
+            if run_cuda_memcheck and ppid_name != "cuda-memcheck":
+                path = os.path.realpath(test_fn.__globals__["__file__"])
+                # get path of current file
+                env = {"PATH": os.environ["PATH"], "PYTORCH_NO_CUDA_MEMORY_CACHING": "1"}
+                assert 'request' in kwargs, "memcheck'ed test must have a (possibly unused) `request` fixture"
+                test_id = kwargs['request'].node.callspec.id
+                cmd = f"{path}::{test_fn.__name__}[{test_id}]"
+                out = subprocess.run(["cuda-memcheck", "pytest", "-vs", cmd], capture_output=True, env=env)
+                assert out.returncode == 0, "cuda-memcheck returned an error: bounds checking failed"
+                assert "ERROR SUMMARY: 0 errors" in str(out.stdout)
+            else:
+                test_fn(*args, **kwargs)
+
+        return wrapper
+
+    return decorator
+
+
+@contextmanager
+def set_gpu_clock(ref_sm_clock=1350, ref_mem_clock=1215):
+    try:
+        subprocess.check_output(["nvidia-smi", "-i", "0", "-pm", "1"])
+        subprocess.check_output([
+            "nvidia-smi",
+            "-i",
+            "0",
+            f"--lock-gpu-clocks={ref_sm_clock},{ref_sm_clock}",
+        ])
+        subprocess.check_output([
+            "nvidia-smi",
+            "-i",
+            "0",
+            f"--lock-memory-clocks={ref_mem_clock},{ref_mem_clock}",
+        ])
+        cur_sm_clock = nvsmi(["clocks.current.sm"])[0]
+        cur_mem_clock = nvsmi(["clocks.current.memory"])[0]
+        assert abs(cur_sm_clock - ref_sm_clock) < 10, f"GPU SMs must run at {ref_sm_clock} MHz"
+        assert abs(cur_mem_clock - ref_mem_clock) < 10, f"GPU SMs must run at {ref_mem_clock} MHz"
+        tflops = 1e-6 * 2 * 108 * 4 * 256 * ref_sm_clock
+        gbps = 640 * 2 * ref_mem_clock * 1e-3
+        yield tflops, gbps
+    finally:
+        subprocess.check_output(["nvidia-smi", "-i", "0", "-pm", "0"])
+        subprocess.check_output(["nvidia-smi", "-i", "0", "-rgc"])
+        subprocess.check_output(["nvidia-smi", "-i", "0", "-rmc"])
+
+
+def get_max_simd_tflops(dtype, clock_rate, device=None):
+    import torch
+
+    from .runtime import driver
+    if not device:
+        device = torch.cuda.current_device()
+
+    num_subcores = driver.active.utils.get_device_properties(device)["multiprocessor_count"] * 4
+    capability = torch.cuda.get_device_capability()
+    if capability[0] < 8:
+        if dtype == torch.float32:
+            ops_per_sub_core = 32  # 2*16
+        elif dtype == torch.float16:
+            ops_per_sub_core = 64
+        else:
+            raise RuntimeError("dtype not supported")
+    else:
+        if dtype == torch.float32:
+            ops_per_sub_core = 32
+        elif dtype in [torch.float16, torch.bfloat16]:
+            ops_per_sub_core = 64
+        else:
+            raise RuntimeError("dtype not supported")
+    tflops = num_subcores * clock_rate * ops_per_sub_core * 1e-9
+    return tflops