import contextlib import copy import inspect import itertools from abc import ABC, abstractmethod from dataclasses import dataclass, field from functools import lru_cache from typing import Any, Callable, Dict, List, Set, Tuple, Union import torch from tensorrt_llm.bindings.internal.runtime import delay_kernel from tensorrt_llm.logger import logger @dataclass(kw_only=True) class TuningConfig: """Configuration for autotuning. This class specifies all the tuning configurations for a single tuning process. Args: dynamic_tensors (Tuple[Tuple[int, int, Tuple[Union[Tuple[int], Callable], Callable]]]): how different tensor dimensions should be tuned to optimize performance. It allows defining which input tensor dimensions are dynamic and how they should be tuned by providing shape generators and rounding rules. A tuple specifying tuning rules: - First level key: Input tensor index (0-based) - Second level key: Dimension index to tune (0-based) - Value: Tuple of (shape_generator, round_rule) where: - shape_generator: List of values to try or function generating values - round_rule: Function to round dimensions to valid values during inference Example: >>> config = TuningConfig( ... dynamic_tensors=( ... (0, 1, ((32, 64, 128), lambda x: ((x + 31) // 32) * 32)), ... ) ... ) constraints (Tuple[Tuple[int, int, Callable]]): A tuple specifying constraints on the dimensions: - First level key: Input tensor index (0-based) - Second level key: Dimension index to constrain (0-based) - Value: Function to apply to the dimension Example: >>> config = TuningConfig( ... constraints=( ... (1, 2, lambda shapes: shapes[0][0] * 2), # constrained dimension index and constraint function ... ) ... ) """ dynamic_tensors: Tuple[Tuple[int, int, Tuple[Union[Tuple[int], Callable], Callable]]] = () constraints: Tuple[Tuple[int, int, Callable]] = () @dataclass(unsafe_hash=True) class StaticDim: val: int def _opt(self): return self.val @dataclass(unsafe_hash=True) class DynamicDim: '''Range of one dimension''' min: int opt: int max: int def _opt(self): return self.opt Dim = Union[DynamicDim, StaticDim] @dataclass class OptimizationProfile: '''Ranges of all tensors, all dimension ''' shapes: List[List[Dim]] def get_hash_key(self): return self.get_opt_shapes() def get_opt_shapes(self): '''Only the opt shapes are considered as hash key ''' # TODO: remove duplicate shape generation opt_shapes = [] for t in self.shapes: opt_shapes.append(tuple([d._opt() for d in t])) return tuple(opt_shapes) #TODO: can/shall we use the torch builtin FakeTensor class? @dataclass class FakeTensor: dtype: torch.dtype device: torch.device shape: List[Dim] class TunableRunner(ABC): @abstractmethod def get_valid_tactics(self, inputs: List[FakeTensor]) -> List[int]: """One tactic corresponding to one cuda kernel normally, but how to interpret the meaning of tactic is pure internal details of the runner. The autotuner will just pass the tactic value to the forward w/o any knowledge on what the tactic means. tactic==-1 has special meaning, means the fallback kernel which should be able to implement any shapes This fallback tactic is needed for 2 reasons: * when the autotuner cannot find a valid tactic in it's cache. * in eager mode, w/o autotunning the custom op should have at least one kernel, which makes the autotuning process an optional process, such that user can opt out. We choose not to have a standalone can_implement function, the tactics returned by get_valid_tactics should return valid kernel for these given input tensors. """ return [-1] def __call__(self, inputs, **kwargs): return self.forward(inputs, **kwargs) @abstractmethod def forward( self, /, # tensors are position only inputs: List[torch.Tensor], *, # all others are keyword args only tactic: int = -1, do_preparation: bool = False) -> Any: """Forward pass for tunable runners. Args: inputs: List of input tensors (position-only argument) tactic: Integer ID specifying which implementation tactic to use. -1 (default) represents the fallback tactic that must be implemented to handle any input shapes when autotuning is disabled. do_preparation: When True, allows one-time setup operations to be performed before tactic evaluation begins. These operations are excluded from the performance measurements during autotuning. Notice that anything prepared in this phase should be persistent in the forward and can be accessed by the following forward calls. Returns: Any: Output of the forward pass """ raise NotImplementedError @lru_cache(maxsize=1000) def find_nearest_profile( self, shapes: Tuple[torch.Size], dynamic_tensors: Tuple[Tuple[int, int, Tuple[Union[Tuple[int], Callable], Callable]]], constraints: Tuple[Tuple[int, int, Callable]]) -> Tuple: """Find the nearest optimization profile for given inputs User can define their own nearest profile generation method to reduce the host overhead. Args: shapes: Tuple of input tensor shapes dynamic_tensors: Tuple of dynamic tensor dimensions constraints: Tuple of constraints Return: Tuple: A tuple containing: - attributes: Tuple of runner attributes, sorted. - profile: Tuple of input tensor shapes """ base_profile = OptimizationProfile([[StaticDim(x) for x in s] for s in shapes]) for input_idx, dim_idx, (_, shape_round_rule) in dynamic_tensors: dim_val = base_profile.shapes[input_idx][dim_idx].val nearest_opt_shape = shape_round_rule(dim_val) base_profile.shapes[input_idx][dim_idx] = StaticDim( nearest_opt_shape) # Adjust the profile to satisfy the constraints for input_idx, dim_idx, constraint in constraints: min_value = 0 max_value = base_profile.shapes[input_idx][dim_idx].val base_profile.shapes[input_idx][dim_idx] = DynamicDim( min_value, constraint(base_profile.get_opt_shapes()), max_value) return base_profile.get_opt_shapes() def get_cache_key( self, custom_op: str, input_shapes: Tuple[torch.Size], tuning_config: TuningConfig, ) -> Tuple: """Generate a cache key for the given custom operation, runner, inputs, and profile. Args: custom_op (str): Name of the custom operation runner (TunableRunner): Runner implementation profile (OptimizationProfile): Optimization profile Returns: Tuple[str, str, Tuple, Tuple]: A tuple containing: - custom_op: Operation name - runner_key: Runner class name - attribute_key: Tuple of runner attributes - profile_key: Profile hash key """ nearest_profile = self.find_nearest_profile( shapes=input_shapes, dynamic_tensors=tuning_config.dynamic_tensors, constraints=tuning_config.constraints) return (self.get_cache_key_general(custom_op), self.get_cache_key_specifc(nearest_profile)) def get_cache_key_general(self, custom_op: str) -> Tuple: """Generate the general part of cache key. Args: custom_op: Operation name Return: Tuple: A tuple containing: - custom_op: Operation name - runner_key: Runner class name """ return custom_op, self.__class__.__name__ def get_cache_key_specifc(self, profile: Tuple) -> Tuple: """Generate the specific part of cache key. User can define their own cache key assembly method to reduce the host overhead. Args: profile: Tuple of input tensor shapes Return: Tuple: A tuple containing: - attributes: Tuple of runner attributes, sorted. - profile: Tuple of input tensor shapes """ attributes = { k: v for k, v in self.__dict__.items() if not callable(v) and not k.startswith("_") } attribute_key = tuple(attributes[key] for key in sorted(attributes.keys())) return attribute_key, profile @contextlib.contextmanager def autotune(tune_mode: bool = True): old_mode = AutoTuner.get().is_tuning_mode AutoTuner.get().is_tuning_mode = tune_mode autotune_enabled = tune_mode and not old_mode if autotune_enabled: logger.info("[Autotuner]: Autotuning process starts ...") try: yield finally: AutoTuner.get().is_tuning_mode = old_mode if autotune_enabled: logger.info("[Autotuner]: Autotuning process ends") @dataclass class AutoTunerStatistics: """Statistics collected by the AutoTuner. Attributes: cache_misses (int): Number of cache misses requiring fallback cache_miss_config_collection (Dict[str, Set[OptimizationProfile]]): Collection of configs that caused cache misses failed_profiling_count (Dict[str, int]): Number of failed profiling attempts per operation tuned_op_total_configs (Dict[str, int]): Total configurations tried per operation tuned_op_successful_configs (Dict[str, int]): Successful configurations per operation """ cache_misses: int = 0 cache_miss_config_collection: Dict[str, Set[tuple]] = field(default_factory=dict) failed_profiling_count: Dict[str, Set[Tuple[str, TunableRunner, OptimizationProfile]]] = field( default_factory=dict) tuned_op_total_configs: Dict[str, int] = field(default_factory=dict) tuned_op_successful_configs: Dict[str, int] = field(default_factory=dict) def __str__(self) -> str: """Return a string representation of collected statistics. """ stats_str = "" stats_str += f"Cache misses: {self.cache_misses}\n" if self.cache_miss_config_collection: stats_str += "Cache miss config collection:\n" for op, profiles in sorted( self.cache_miss_config_collection.items()): stats_str += f" {op}:\n" for profile in sorted(profiles, key=str): stats_str += f" - Config: {profile}\n" if self.tuned_op_total_configs: stats_str += "Tuned operations:\n" for op in sorted(self.tuned_op_total_configs.keys()): total = self.tuned_op_total_configs[op] successful = self.tuned_op_successful_configs.get(op, 0) failed = len(self.failed_profiling_count.get(op, set())) success_rate = (successful / total * 100) if total > 0 else 0 stats_str += f" {op}:\n" stats_str += f" - Total configs tried: {total}\n" stats_str += f" - Successful configs: {successful}\n" stats_str += f" - Failed profiling count: {failed}\n" if failed > 0: stats_str += f" - Failed profiling combinations:\n" for failed_key in self.failed_profiling_count[op]: stats_str += f" - {failed_key}\n" stats_str += f" - Success rate: {success_rate:.1f}%\n" return stats_str class AutoTuner: """AutoTuner for optimizing TensorRT-LLM operations. This class handles automatic performance tuning of tensor operations by profiling different implementations and caching the best performing configurations. Args: warmup (int): Number of warmup iterations before profiling (default: 3) repeat (int): Number of profiling iterations for averaging (default: 10) stream_delay_micro_secs (int): Delay on CUDA stream before the profiled kernel runs in microseconds (default: 1000) """ _instance = None def __init__(self, warmup=3, repeat=10, stream_delay_micro_secs=1000): self.repeat = repeat self.warmup = warmup self.stream_delay_micro_secs = stream_delay_micro_secs self.profiling_cache = {} self.is_tuning_mode = False # Add statistics tracking self.stats = AutoTunerStatistics() self.profiling_debug = True @classmethod def get(cls): if cls._instance is None: cls._instance = AutoTuner() return cls._instance def search_cache( self, custom_op: str, runners: List[TunableRunner], input_shapes: Tuple[torch.Size], tuning_config: TuningConfig, ) -> Tuple[bool, int, int, OptimizationProfile]: """Search for cached profiling results matching the current configuration. Args: custom_op (str): The name of the custom operation to be tuned runners (List[TunableRunner]): List of candidate implementations to profile profile (OptimizationProfile): Optimization profile Returns: A tuple containing: [is_cache_hit, runner_id, tactic, stored_profile] """ for r in runners: cache_key = r.get_cache_key(custom_op, input_shapes, tuning_config) if cache_key in self.profiling_cache: return True, *self.profiling_cache[cache_key] return False, 0, -1, None def choose_one(self, custom_op: str, runners: List[TunableRunner], tuning_config: TuningConfig, inputs: List[torch.Tensor], **kwargs) -> Tuple[TunableRunner, int]: """Choose the best runner and tactic combination through performance profiling. Args: custom_op (str): The name of the custom operation to be tuned runners (List[TunableRunner]): List of candidate implementations to profile tuning_config (TuningConfig): Configuration for the tuning process inputs (List[torch.Tensor]): Input tensors for profiling **kwargs: Arbitrary keyword arguments, will be passed to get_valid_tactics and forward method of each runner Returns: Tuple[TunableRunner, int]: A tuple containing: - The selected runner implementation - The best tactic ID for that runner (-1 if using fallback) Note: The method profiles different implementations and tactics to find the optimal combination based on performance measurements. It caches results to avoid redundant profiling of the same configuration. Although runners[0] with tactic=-1 is always treated as the fallback runner. Runner authors are suggested to provide a fallback implementation for each runner to avoid potential issues. """ input_shapes = tuple(t.shape for t in inputs) # Early return if it's not tuning, use cache found one or fallback one if not self.is_tuning_mode: is_cache_hit, runner_id, tactic, stored_profile = self.search_cache( custom_op, runners, input_shapes, tuning_config) runner = runners[runner_id] # TODO: check the stored runner and tactic can implement this shape here # Should not directly try (runner, tactic) here, or it will hurt a lot of inference perf. # Record the cache miss config. # Expect no cache miss in inference. Thus, any cache miss should be recorded. if not is_cache_hit: self.stats.cache_misses += 1 if custom_op not in self.stats.cache_miss_config_collection: self.stats.cache_miss_config_collection[custom_op] = set() self.stats.cache_miss_config_collection[custom_op].add( input_shapes) logger.debug( f"[AutoTunner]: Using fallback tactic for {custom_op} with input shapes {input_shapes}" ) assert runner == runners[0] \ and tactic == -1, f"Should use fallback runner {runners[0]} and tactic {-1}, but got runner {runner} and tactic {tactic}" return runner, tactic assert len(runners) > 0, "At least one runner is required" assert all([isinstance(r, TunableRunner) for r in runners]), \ "All Given runners must be subclass of TunableRunner" profiles = self._optimization_profiles(tuning_config.dynamic_tensors, tuning_config.constraints, inputs) # Record the total configs to try self.stats.tuned_op_total_configs[custom_op] = len(profiles) for p in profiles: tensors = self._prepare_input_tensors(p, inputs) is_cache_hit, runner, tactic, _ = self.search_cache( custom_op, runners, p.get_opt_shapes(), tuning_config) if not is_cache_hit: min_time = float('inf') # Initialize runner and tactic as None in case of no valid tactic or runners are found runner, tactic = None, None for runner_id, r in enumerate(runners): # TODO: use FakeTensor here. valid_tactics = r.get_valid_tactics(tensors) runner_arg_names = { p.name for p in inspect.signature( r.forward).parameters.values() } if "do_preparation" in runner_arg_names and len( valid_tactics) > 0: r(tensors, tactic=-1, do_preparation=True, **kwargs) for tac in valid_tactics: try: time_measured = self._profile_single_kernel( r, tensors, tac, **kwargs) except Exception as e: logger.error( f"[Autotuner]: Failed when profiling {r} {tac}, shapes={[t.size() for t in tensors]}. Error occurred: {e}" ) # Record the failed profiling combinations if custom_op not in self.stats.failed_profiling_count: self.stats.failed_profiling_count[ custom_op] = set() self.stats.failed_profiling_count[custom_op].add( r.get_cache_key(custom_op, p.get_opt_shapes(), tuning_config)) # Set time_measured to inf to notify the failure of the tactic. This can happen when `get_valid_tactics` mistakenly return wrong tactics # or some runtime error occurs during profiling. time_measured = float('inf') if time_measured < min_time: min_time = time_measured runner, tactic = r, tac if runner is not None: # At least one valid (runner, tactic) pair is found cache_key = runner.get_cache_key(custom_op, p.get_opt_shapes(), tuning_config) # inspect call stack self.profiling_cache[cache_key] = (runner_id, tactic, p) self.stats.tuned_op_successful_configs[ custom_op] = self.stats.tuned_op_successful_configs.get( custom_op, 0) + 1 logger.debug( f"[Autotuner]: profiling chosen runner: {runner} {tactic} for {cache_key}" ) # Get the best runner and tactic from cache # If no valid tactic is found, the fallback runner and tactic will be used _, runner_id, tactic, _ = self.search_cache(custom_op, runners, input_shapes, tuning_config) return runners[runner_id], tactic def _profile_single_kernel(self, runner: TunableRunner, inputs: List[torch.Tensor], tactic: int, **kwargs) -> float: """Profile a single kernel implementation for performance measurement. Args: runner (TunableRunner): The runner implementation to profile inputs (List[torch.Tensor]): Input tensors for the kernel tactic (int): Tactic ID to use for this profiling run Returns: Average execution time in milliseconds Note: The method performs warmup runs, then measures multiple iterations to get an average execution time. Stream synchronization and delays are used to ensure accurate timing. """ stream = torch.cuda.current_stream() # warm up, no timing for _ in range(self.warmup): runner(inputs, tactic=tactic, **kwargs) stream.synchronize() # Delay the profiled kernel launch to eliminate affects of host time overhead in profiling. # TODO: This is build time sensitive, O(tactic_num * impl_num * num_profile * tunable_ops) # Consider apply a preprofiling to estimate the kernel execution time, then decide the necessity. delay_kernel(self.stream_delay_micro_secs, stream) start = torch.cuda.Event(enable_timing=True) end = torch.cuda.Event(enable_timing=True) start.record(stream=stream) for _ in range(self.repeat): runner(inputs, tactic=tactic, **kwargs) end.record(stream=stream) stream.synchronize() avg_time = start.elapsed_time(end) / self.repeat logger.debug( f"[Autotuner]: profiling {runner} {tactic}, shapes={[t.size() for t in inputs]}, avg_time {avg_time}" ) return avg_time def _optimization_profiles( self, dynamic_tensors: Tuple[Tuple[int, int, Tuple[Union[Tuple[int], Callable], Callable]]], constraints: Tuple[Tuple[int, int, Callable]], inputs: List[torch.Tensor]) -> List[OptimizationProfile]: """Generate optimization profiles for autotuning. Args: dynamic_tensors (Tuple[Tuple[int, int, Tuple[Union[Tuple[int], Callable], Callable]]]): Tuple specifying which dimensions to tune constraints (Tuple[Tuple[int, int, Callable]]): Tuple specifying constraints on the dimensions inputs (List[torch.Tensor]): List of input tensors Returns: List of OptimizationProfile objects representing different configurations Note: This method performs a cartesian product of all possible dimension combinations specified in dynamic_tensors. """ # every dimension created from the concrete input tensor shape # generate some dynamic dimension description based on the dynamic_tensors base_profile = OptimizationProfile([[StaticDim(x) for x in t.size()] for t in inputs]) generated_profiles: List[OptimizationProfile] = [] dynamic_dims = [] for input_idx, dim_idx, (shape_generater, shape_round_rule) in dynamic_tensors: assert inspect.isfunction(shape_generater) or isinstance(shape_generater, (list, tuple)), \ "The given dynamic dimension must provide a opt value generation function or a list of opt values" if inspect.isfunction(shape_generater): opt_shapes = shape_generater( base_profile.shapes[input_idx][dim_idx].val) else: opt_shapes = shape_generater dynamic_dims.append((input_idx, dim_idx, opt_shapes)) # grid search, do cartesian product for all the dynamic axis dim_grids = itertools.product(*[d[-1] for d in dynamic_dims]) for opt_point in dim_grids: p = copy.deepcopy(base_profile) for pos, (input_idx, dim_idx, _) in enumerate(dynamic_dims): opt_value = opt_point[pos] #TODO: fix me, how to set the min and max? min_value = 0 max_value = base_profile.shapes[input_idx][dim_idx].val p.shapes[input_idx][dim_idx] = DynamicDim( min_value, opt_value, max_value) # Adjust the profile to satisfy the constraints for input_idx, dim_idx, constraint in constraints: min_value = 0 max_value = base_profile.shapes[input_idx][dim_idx].val p.shapes[input_idx][dim_idx] = DynamicDim( min_value, constraint(p.get_opt_shapes()), max_value) generated_profiles.append(p) logger.debug(f"[Autotuner]: generated profile: {p}") return generated_profiles def _create_tensor_like(self, origin_tensor: torch.Tensor, dims: List[Dim]) -> torch.Tensor: """Create a new tensor matching the properties of the original tensor. Args: origin_tensor (torch.Tensor): Template tensor to match dims (List[Dim]): List of dimensions for the new tensor Returns: New tensor with specified dimensions and matching properties Note: Creates a zero tensor with the same dtype and device as the original, but with dimensions specified by the dims parameter. """ dtype = origin_tensor.dtype device = origin_tensor.device shapes = [] for d in dims: if isinstance(d, StaticDim): shapes.append(d.val) else: # TODO: how to make sure the created Tensor has the min/max info assert isinstance(d, DynamicDim) shapes.append(d.opt) # TODO: FIXME, sometimes the content of the tensor can affect the performance, like MOE # One solution is to manituplate the tensor content to make it more like the real data # during the tuning process. This can by controlled in the preparation phase by the runner. return torch.zeros(shapes, dtype=dtype, device=device) def _prepare_input_tensors( self, profile: OptimizationProfile, inputs: List[torch.Tensor]) -> List[torch.Tensor]: tensors = [] for i, p in enumerate(profile.shapes): if any(isinstance(d, DynamicDim) for d in p): tensor = self._create_tensor_like(inputs[i], p) else: tensor = inputs[i] tensors.append(tensor) return tensors def clear_cache(self) -> None: """Clear the profiling cache.""" self.profiling_cache.clear() def reset_statistics(self) -> None: """Reset all statistics counters.""" self.stats = AutoTunerStatistics()