import math from types import MethodType from typing import Any, Dict, List, Optional, Tuple, Union from .state import PartialState from .utils import ( calculate_maximum_sizes, convert_bytes, copy_tensor_to_devices, ignorant_find_batch_size, infer_auto_device_map, is_pippy_available, pad_input_tensors, send_to_device, ) if is_pippy_available(): from pippy.IR import Pipe, PipeSplitWrapper, annotate_split_points from pippy.PipelineStage import PipelineStage def generate_device_map(model, num_processes: int = 1, no_split_module_classes=None, max_memory: dict = None): """ Calculates the device map for `model` with an offset for PiPPy """ if num_processes == 1: return infer_auto_device_map(model, no_split_module_classes=no_split_module_classes, clean_result=False) if max_memory is None: model_size, shared = calculate_maximum_sizes(model) # Split into `n` chunks for each GPU memory = (model_size + shared[0]) / num_processes memory = convert_bytes(memory) value, ending = memory.split(" ") # Add a chunk to deal with potential extra shared memory instances memory = math.ceil(float(value)) * 1.1 memory = f"{memory} {ending}" max_memory = {i: memory for i in range(num_processes)} device_map = infer_auto_device_map( model, max_memory=max_memory, no_split_module_classes=no_split_module_classes, clean_result=False, ) return device_map def find_pippy_batch_size(args, kwargs): found_batch_size = None if args is not None: for arg in args: found_batch_size = ignorant_find_batch_size(arg) if found_batch_size is not None: break if kwargs is not None and found_batch_size is None: for kwarg in kwargs.values(): found_batch_size = ignorant_find_batch_size(kwarg) if found_batch_size is not None: break return found_batch_size def build_pipeline(model, split_points, args, kwargs, num_chunks): """ Attaches the split points to the model based on `self.device_map` and generates a `PipelineStage`. Requires passing in needed `args` and `kwargs` as the model needs on the CPU. Users can pass in custom `num_chunks` as an optional hyper-parameter. By default will use `AcceleratorState.num_processes` """ # We need to annotate the split points in the model for PiPPy state = PartialState() annotate_split_points(model, {split_point: PipeSplitWrapper.SplitPoint.BEGINNING for split_point in split_points}) found_batch_size = find_pippy_batch_size(args, kwargs) if found_batch_size != num_chunks: args = pad_input_tensors(args, found_batch_size, num_chunks) kwargs = pad_input_tensors(kwargs, found_batch_size, num_chunks) pipe = Pipe.from_tracing(model, num_chunks=num_chunks, example_args=args, example_kwargs=kwargs) stage = PipelineStage(pipe, state.local_process_index, device=state.device) return stage def pippy_forward(forward, num_chunks, gather_output, *args, **kwargs): state = PartialState() output = None if state.num_processes == 1: output = forward(*args, **kwargs) elif state.is_local_main_process: found_batch_size = find_pippy_batch_size(args, kwargs) if found_batch_size is None: raise ValueError("Could not find batch size from args or kwargs") else: if found_batch_size != num_chunks: args = pad_input_tensors(args, found_batch_size, num_chunks) kwargs = pad_input_tensors(kwargs, found_batch_size, num_chunks) forward(*args, **kwargs) elif state.is_last_process: output = forward() else: forward() if gather_output: # Each node will get a copy of the full output which is only on the last GPU output = copy_tensor_to_devices(output) return output def prepare_pippy( model, split_points: Optional[Union[str, List[str]]] = "auto", no_split_module_classes: Optional[List[str]] = None, example_args: Optional[Tuple[Any]] = (), example_kwargs: Optional[Dict[str, Any]] = None, num_chunks: Optional[int] = None, gather_output: Optional[bool] = False, ): """ Wraps `model` for pipeline parallel inference. Args: model (`torch.nn.Module`): A model we want to split for pipeline-parallel inference split_points (`str` or `List[str]`, defaults to 'auto'): How to generate the split points and chunk the model across each GPU. 'auto' will find the best balanced split given any model. Should be a list of layer names in the model to split by otherwise. no_split_module_classes (`List[str]`): A list of class names for layers we don't want to be split. example_args (tuple of model inputs): The expected inputs for the model that uses order-based inputs. Recommended to use this method if possible. example_kwargs (dict of model inputs) The expected inputs for the model that uses dictionary-based inputs. This is a *highly* limiting structure that requires the same keys be present at *all* inference calls. Not recommended unless the prior condition is true for all cases. num_chunks (`int`, defaults to the number of available GPUs): The number of different stages the Pipeline will have. By default it will assign one chunk per GPU, but this can be tuned and played with. In general one should have num_chunks >= num_gpus. gather_output (`bool`, defaults to `False`): If `True`, the output from the last GPU (which holds the true outputs) is sent across to all GPUs. """ if not is_pippy_available(): raise ImportError( "`pippy` was not found to be installed on your system. Please " "install using `pip install torchpippy` or ensure you have at least version 0.2.0" ) state = PartialState() example_args = send_to_device(example_args, "cpu") example_kwargs = send_to_device(example_kwargs, "cpu") if num_chunks is None: num_chunks = state.num_processes if split_points == "auto": device_map = generate_device_map(model, num_chunks, no_split_module_classes=no_split_module_classes) split_points = [] for i in range(1, num_chunks): split_points.append(next(k for k, v in device_map.items() if v == i)) model.hf_split_points = split_points stage = build_pipeline(model, split_points, example_args, example_kwargs, num_chunks) model._original_forward = model.forward model._original_call = model.__call__ model.pippy_stage = stage model.hf_split_points = split_points def forward(*args, **kwargs): return pippy_forward(stage.forward, num_chunks, gather_output, *args, **kwargs) # To act like a decorator so that it can be popped when doing `extract_model_from_parallel` # Note: creates an infinite recursion loop with `generate` model_forward = MethodType(forward, model) forward.__wrapped__ = model_forward model.forward = forward return model