zaochuan5854
8e19a184f6
- Updated TRTAnimaDiffuser to require only the engine and onnx_model parameters, simplifying its initialization. - Refactored TRTDiffuser to remove engine_path handling, now requiring an engine directly. - Adjusted instantiation of TRTAnimaDiffuser in TRTLoader to match new constructor signature. - Enhanced code clarity by removing unnecessary conditional checks for engine initialization.
304 lines
14 KiB
Python
304 lines
14 KiB
Python
from typing import Any, Optional, cast, Literal
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import numpy as np
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from tqdm import tqdm
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import torch
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import tensorrt as trt
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import comfy.lora
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import comfy.model_management
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from ..definitions import SupportedModelType, WeightsNameMap, WeightsShapeMap, PatchType, ModelInputNames, ModelInputMapping, trt_logger
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from ..trt_utils import trt_datatype_to_torch, torch_dtype_to_trt
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SupportedModelName = [e.name for e in SupportedModelType]
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class TRTDiffuser:
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def __init__(self, engine: trt.ICudaEngine, weight_map: Optional[WeightsNameMap] = None, shape_map: Optional[WeightsShapeMap] = None) -> None:
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self.engine = engine
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trt_dtype = self.engine.get_tensor_dtype(self.engine.get_tensor_name(0))
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self.dtype = trt_datatype_to_torch(trt_dtype)
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self.context = self.engine.create_execution_context()
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self.model_input_names: ModelInputNames = {
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"latent": "",
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"timestep": "",
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"context": None,
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"vector_cond": None
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}
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self.input_aliases_map: ModelInputMapping = {
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"latent": ["latent", "x", "cx"],
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"timestep": ["timestep", "timesteps", "t"],
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"context": ["context", "ctx"],
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"vector_cond": ["vector_cond", "y"]
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}
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self.weight_mapping = weight_map
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self.shape_mapping = shape_map
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self.model_output_names: list[str] = []
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self.patches: dict[str, list[PatchType]] = {}
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self.source_state_dict: dict[str, torch.Tensor] = {}
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self._last_refit_layers: set[str] = set()
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self.refit_compute_device: torch.device = cast(torch.device, comfy.model_management.get_torch_device())
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self._rename_inputs()
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def _rename_inputs(self):
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engine_model_input_names: list[str] = []
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engine_model_output_names: list[str] = []
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# tensorrt is 8.5+ by requirement
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num_io_tensors = self.engine.num_io_tensors
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for i in range(num_io_tensors):
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name = self.engine.get_tensor_name(i)
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mode = self.engine.get_tensor_mode(name)
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if mode == trt.TensorIOMode.INPUT:
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engine_model_input_names.append(name)
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elif mode == trt.TensorIOMode.OUTPUT:
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engine_model_output_names.append(name)
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for key, aliases in self.input_aliases_map.items():
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for alias in cast(list[str], aliases):
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if alias in engine_model_input_names:
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self.model_input_names[key] = alias
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break
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self.model_output_names = engine_model_output_names
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def __call__(self, latent: torch.Tensor, timestep: torch.Tensor, **kwargs: Any) -> torch.Tensor:
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model_inputs: dict[str, torch.Tensor] = {self.model_input_names["latent"]: latent, self.model_input_names["timestep"]: timestep}
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for arg, value in kwargs.items():
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for key, aliases in self.input_aliases_map.items():
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if arg in cast(list[str], aliases) and self.model_input_names[key] is not None:
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model_inputs[self.model_input_names[key]] = value
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break
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batch_size = latent.shape[0]
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dims = self.engine.get_tensor_profile_shape(self.engine.get_tensor_name(0), 0)
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min_batch = dims[0][0]
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# opt_batch = dims[1][0]
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max_batch = dims[2][0]
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curr_split_batch = 1
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for i in range(max_batch, min_batch - 1, -1):
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if batch_size % i == 0:
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curr_split_batch = batch_size // i
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break
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self._set_bindings_shape(model_inputs, curr_split_batch)
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model_inputs_converted: dict[str, torch.Tensor] = {}
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for arg, tensor in model_inputs.items():
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data_type = self.engine.get_tensor_dtype(arg)
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model_inputs_converted[arg] = tensor.to(dtype=trt_datatype_to_torch(data_type))
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assert len(self.model_output_names) == 1, "Only single output is supported"
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output_binding_name = self.model_output_names[0]
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out_shape_tuple = self.engine.get_tensor_shape(output_binding_name)
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out_shape: list[int] = list(out_shape_tuple)
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for idx in range(len(out_shape)):
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if out_shape[idx] == -1:
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out_shape[idx] = latent.shape[idx]
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else:
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if idx == 0:
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out_shape[idx] *= curr_split_batch
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out = torch.empty(out_shape,
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device=latent.device,
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dtype=trt_datatype_to_torch(self.engine.get_tensor_dtype(output_binding_name)))
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model_inputs_converted[output_binding_name] = out
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stream = torch.cuda.default_stream(latent.device)
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for i in range(curr_split_batch):
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for arg, tensor in model_inputs_converted.items():
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chunk_size = tensor.shape[0] // curr_split_batch
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self.context.set_tensor_address(arg, tensor[chunk_size * i:].data_ptr())
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self.context.execute_async_v3(stream_handle=stream.cuda_stream)
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return out
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def _set_bindings_shape(self, inputs: dict[str, torch.Tensor], split_batch: int) -> None:
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for k, tensor in inputs.items():
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shape = list(tensor.shape)
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shape[0] = shape[0] // split_batch
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self.context.set_input_shape(k, shape)
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def refit(self):
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self._validate_refit_support()
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refitter = trt.Refitter(self.engine, trt_logger)
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keep_weights: dict[str, torch.Tensor] = {}
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target_layers = list(self._last_refit_layers)
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for layer in self.patches:
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if self._should_refit_layer(layer) and layer not in target_layers:
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target_layers.append(layer)
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pbar = tqdm(target_layers, desc="[TRT] Calculating and Refitting LoRA Layers", total=len(target_layers))
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weight_device_map = self._schedule_device(target_layers)
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cpu_buffers = self._create_cpu_buffer([layer for layer in target_layers if weight_device_map[layer] == "cpu"])
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event = torch.cuda.Event()
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event.record()
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try:
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for layer in pbar:
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pbar.set_postfix({"layer": layer.split('.')[-1]}) # pyright: ignore[reportUnknownMemberType]
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base_weight = self._build_base_weight(layer)
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patch_list = self.patches.get(layer, [])
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if patch_list:
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base_weight = self._apply_patches(layer, base_weight, patch_list)
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final_weight = self._prepare_final_weight(layer, base_weight)
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if weight_device_map[layer] == "cpu":
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cpu_buffers[layer].copy_(final_weight, non_blocking=True)
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keep_weights[layer] = cpu_buffers[layer]
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else:
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host_weight = self._set_refit_weights(refitter, layer, final_weight)
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keep_weights[layer] = host_weight
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finally:
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pbar.close()
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event.record()
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event.synchronize()
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cpu_layers = [layer for layer in target_layers if weight_device_map[layer] == "cpu"]
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for cpu_layer in cpu_layers:
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host_weight = self._set_refit_weights(refitter, cpu_layer, cpu_buffers[cpu_layer])
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self._commit_refit(refitter, keep_weights)
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self._last_refit_layers = {layer for layer in target_layers if self._should_refit_layer(layer)}
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self._finalize_refit(keep_weights)
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def _validate_refit_support(self) -> None:
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if self.patches and not (self.weight_mapping and self.shape_mapping):
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raise RuntimeError("This model does not support LoRA.")
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if (self.patches or self._last_refit_layers) and not self.source_state_dict:
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raise RuntimeError("Source state_dict is required for TensorRT refit but not found.")
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def _should_refit_layer(self, layer: str) -> bool:
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assert self.weight_mapping is not None, "Weight mapping is required for refitting but not found."
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return layer in self.weight_mapping
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def _schedule_device(self, layers: list[str]) -> dict[str, Literal["cpu", "cuda"]]:
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assert self.shape_mapping is not None, "Shape mapping is required for refitting but not found."
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free, _ = torch.cuda.mem_get_info("cuda")
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gpu_memory_margin = 1.8 # Alocate 1.8x the weight size on GPU to account for temporary tensors during refit
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weight_device_map: dict[str, Literal["cpu", "cuda"]] = {}
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# Process layers in reverse to prioritize weights scheduled for CPU by assigning them first.
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# This allows asynchronous transfers to start earlier, maximizing overlap with GPU tasks.
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for layer in reversed(layers):
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shapes = list(self.shape_mapping[layer][0])
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weight_size = self.dtype.itemsize * np.prod(shapes) * gpu_memory_margin
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free -= weight_size
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if free > 0:
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weight_device_map[layer] = "cuda"
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else:
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weight_device_map[layer] = "cpu"
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return weight_device_map
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def _create_cpu_buffer(self, layers: list[str]) -> dict[str, torch.Tensor]:
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assert self.shape_mapping is not None, "Shape mapping is required for refitting but not found."
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alignment = 256
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pointer = 0
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layer_range: dict[str, tuple[int, int]] = {}
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for layer in layers:
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shapes = list(self.shape_mapping[layer][0])
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weight_size = int(self.dtype.itemsize * np.prod(shapes))
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layer_range[layer] = (pointer, pointer + weight_size)
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pointer += (weight_size + alignment - 1) // alignment * alignment
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buffer_size = pointer
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storage = torch.UntypedStorage(buffer_size, device="cpu").pin_memory()
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cpu_buffers: dict[str, torch.Tensor] = {}
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for layer in layers:
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start, end = layer_range[layer]
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buffer = cast(torch.UntypedStorage, storage[start:end])
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cpu_buffers[layer] = torch.tensor(buffer, dtype=self.dtype, device="cpu").view(self.shape_mapping[layer][0])
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return cpu_buffers
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def _build_base_weight(self, layer: str) -> torch.Tensor:
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if layer not in self.source_state_dict:
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raise RuntimeError(f"Source weight for layer {layer} not found.")
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return self.source_state_dict[layer].detach().clone().to(device=self.refit_compute_device, dtype=torch.float32)
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def _apply_patches(self, layer: str, base_weight: torch.Tensor, patch_list: list[PatchType]) -> torch.Tensor:
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def _identity_convert(weight: torch.Tensor, **kwargs: Any) -> torch.Tensor:
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return weight
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original_weights = {
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layer: [(base_weight.detach().clone(), _identity_convert)],
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}
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out_weight: torch.Tensor = cast(torch.Tensor, comfy.lora.calculate_weight( # pyright: ignore[reportUnknownMemberType]
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patch_list,
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base_weight,
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layer,
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intermediate_dtype=torch.float32,
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original_weights=original_weights,
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))
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return out_weight
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def _prepare_final_weight(self, layer: str, base_weight: torch.Tensor) -> torch.Tensor:
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if self.shape_mapping is None or layer not in self.shape_mapping:
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raise RuntimeError(f"Shape mapping is required for refitting layer {layer} but not found.")
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is_transpose = self.shape_mapping[layer][1]
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if is_transpose:
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final_weight = base_weight.T.contiguous()
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else:
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final_weight = base_weight.contiguous()
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# Match engine dtype (FP16/FP32/BF16 depending on engine build).
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return final_weight.to(self.dtype)
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def _set_refit_weights(self, refitter: trt.Refitter, layer: str, final_weight: torch.Tensor) -> torch.Tensor:
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assert self.weight_mapping is not None, "Weight mapping is required for refitting but not found."
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trt_weights = trt.Weights(
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torch_dtype_to_trt(final_weight.dtype),
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final_weight.data_ptr(),
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final_weight.numel(),
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)
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success = refitter.set_named_weights(self.weight_mapping[layer], trt_weights)
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if not success:
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raise RuntimeError(f"Failed to set weights for {layer}")
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return final_weight
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def _commit_refit(self, refitter: trt.Refitter, keep_weights: dict[str, torch.Tensor], num_tries: int = 5, current_try: int = 0) -> None:
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assert self.weight_mapping is not None, "Weight mapping is required for refitting but not found."
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print("[TRT] Committing refit to engine...")
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success = refitter.refit_cuda_engine()
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if success:
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print("[TRT] Engine refit successful.")
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else:
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missing_onnx_weights = refitter.get_missing_weights()
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if current_try >= num_tries or not missing_onnx_weights:
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raise RuntimeError("Final engine refit failed without reporting missing weights.")
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missing_layers = [layer for layer, weight_name in self.weight_mapping.items() if weight_name in missing_onnx_weights]
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for layer in missing_layers:
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if layer not in self.source_state_dict:
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raise RuntimeError(f"Final engine refit failed: Missing weight '{layer}' not found in original state dict.")
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print(f"[TRT] WARNING: Missing weight for '{layer}'. Assigning original weight from state dict.")
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base_weight = self._build_base_weight(layer)
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final_weight = self._prepare_final_weight(layer, base_weight)
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host_weight = self._set_refit_weights(refitter, layer, final_weight)
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keep_weights[layer] = host_weight
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self._commit_refit(refitter, keep_weights, num_tries, current_try + 1)
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def _finalize_refit(self, keep_weights: dict[str, torch.Tensor]) -> None:
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self.patches = {}
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del keep_weights
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def set_source_state_dict(self, state_dict: dict[str, torch.Tensor]) -> None:
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self.source_state_dict = state_dict
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def load_state_dict(self, sd: dict[str, Any], strict: bool = False) -> None:
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pass
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def state_dict(self) -> dict[str, torch.Tensor]:
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return {}
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