435 lines
15 KiB
Python
435 lines
15 KiB
Python
#! fork: https://github.com/NVIDIA/TensorRT/blob/main/demo/Diffusion/models.py
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#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import onnx_graphsurgeon as gs
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import torch
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from onnx import shape_inference
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from polygraphy.backend.onnx.loader import fold_constants
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class Optimizer:
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def __init__(self, onnx_graph, verbose=False):
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self.graph = gs.import_onnx(onnx_graph)
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self.verbose = verbose
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def info(self, prefix):
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if self.verbose:
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print(
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f"{prefix} .. {len(self.graph.nodes)} nodes, {len(self.graph.tensors().keys())} tensors, {len(self.graph.inputs)} inputs, {len(self.graph.outputs)} outputs"
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)
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def cleanup(self, return_onnx=False):
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self.graph.cleanup().toposort()
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if return_onnx:
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return gs.export_onnx(self.graph)
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def select_outputs(self, keep, names=None):
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self.graph.outputs = [self.graph.outputs[o] for o in keep]
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if names:
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for i, name in enumerate(names):
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self.graph.outputs[i].name = name
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def fold_constants(self, return_onnx=False):
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onnx_graph = fold_constants(gs.export_onnx(self.graph), allow_onnxruntime_shape_inference=True)
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self.graph = gs.import_onnx(onnx_graph)
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if return_onnx:
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return onnx_graph
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def infer_shapes(self, return_onnx=False):
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onnx_graph = gs.export_onnx(self.graph)
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if onnx_graph.ByteSize() > 2147483648:
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raise TypeError("ERROR: model size exceeds supported 2GB limit")
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else:
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onnx_graph = shape_inference.infer_shapes(onnx_graph)
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self.graph = gs.import_onnx(onnx_graph)
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if return_onnx:
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return onnx_graph
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class BaseModel:
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def __init__(
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self,
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fp16=False,
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device="cuda",
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verbose=True,
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max_batch_size=16,
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min_batch_size=1,
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embedding_dim=768,
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text_maxlen=77,
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):
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self.name = "SD Model"
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self.fp16 = fp16
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self.device = device
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self.verbose = verbose
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self.min_batch = min_batch_size
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self.max_batch = max_batch_size
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self.min_image_shape = 256 # min image resolution: 256x256
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self.max_image_shape = 1024 # max image resolution: 1024x1024
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self.min_latent_shape = self.min_image_shape // 8
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self.max_latent_shape = self.max_image_shape // 8
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self.embedding_dim = embedding_dim
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self.text_maxlen = text_maxlen
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def get_model(self):
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pass
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def get_input_names(self):
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pass
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def get_output_names(self):
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pass
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def get_dynamic_axes(self):
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return None
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def get_sample_input(self, batch_size, image_height, image_width):
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pass
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def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
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return None
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def get_shape_dict(self, batch_size, image_height, image_width):
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return None
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def optimize(self, onnx_graph):
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opt = Optimizer(onnx_graph, verbose=self.verbose)
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opt.info(self.name + ": original")
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opt.cleanup()
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opt.info(self.name + ": cleanup")
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opt.fold_constants()
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opt.info(self.name + ": fold constants")
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opt.infer_shapes()
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opt.info(self.name + ": shape inference")
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onnx_opt_graph = opt.cleanup(return_onnx=True)
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opt.info(self.name + ": finished")
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return onnx_opt_graph
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def check_dims(self, batch_size, image_height, image_width):
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assert batch_size >= self.min_batch and batch_size <= self.max_batch
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assert image_height % 8 == 0 or image_width % 8 == 0
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latent_height = image_height // 8
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latent_width = image_width // 8
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assert latent_height >= self.min_latent_shape and latent_height <= self.max_latent_shape
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assert latent_width >= self.min_latent_shape and latent_width <= self.max_latent_shape
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return (latent_height, latent_width)
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def get_minmax_dims(self, batch_size, image_height, image_width, static_batch, static_shape):
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min_batch = batch_size if static_batch else self.min_batch
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max_batch = batch_size if static_batch else self.max_batch
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latent_height = image_height // 8
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latent_width = image_width // 8
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min_image_height = image_height if static_shape else self.min_image_shape
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max_image_height = image_height if static_shape else self.max_image_shape
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min_image_width = image_width if static_shape else self.min_image_shape
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max_image_width = image_width if static_shape else self.max_image_shape
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min_latent_height = latent_height if static_shape else self.min_latent_shape
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max_latent_height = latent_height if static_shape else self.max_latent_shape
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min_latent_width = latent_width if static_shape else self.min_latent_shape
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max_latent_width = latent_width if static_shape else self.max_latent_shape
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return (
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min_batch,
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max_batch,
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min_image_height,
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max_image_height,
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min_image_width,
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max_image_width,
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min_latent_height,
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max_latent_height,
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min_latent_width,
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max_latent_width,
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)
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class CLIP(BaseModel):
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def __init__(self, device, max_batch_size, embedding_dim, min_batch_size=1):
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super(CLIP, self).__init__(
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device=device,
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max_batch_size=max_batch_size,
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min_batch_size=min_batch_size,
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embedding_dim=embedding_dim,
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)
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self.name = "CLIP"
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def get_input_names(self):
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return ["input_ids"]
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def get_output_names(self):
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return ["text_embeddings", "pooler_output"]
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def get_dynamic_axes(self):
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return {"input_ids": {0: "B"}, "text_embeddings": {0: "B"}}
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def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
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self.check_dims(batch_size, image_height, image_width)
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min_batch, max_batch, _, _, _, _, _, _, _, _ = self.get_minmax_dims(
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batch_size, image_height, image_width, static_batch, static_shape
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)
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return {
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"input_ids": [
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(min_batch, self.text_maxlen),
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(batch_size, self.text_maxlen),
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(max_batch, self.text_maxlen),
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]
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}
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def get_shape_dict(self, batch_size, image_height, image_width):
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self.check_dims(batch_size, image_height, image_width)
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return {
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"input_ids": (batch_size, self.text_maxlen),
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"text_embeddings": (batch_size, self.text_maxlen, self.embedding_dim),
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}
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def get_sample_input(self, batch_size, image_height, image_width):
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self.check_dims(batch_size, image_height, image_width)
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return torch.zeros(batch_size, self.text_maxlen, dtype=torch.int32, device=self.device)
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def optimize(self, onnx_graph):
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opt = Optimizer(onnx_graph)
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opt.info(self.name + ": original")
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opt.select_outputs([0]) # delete graph output#1
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opt.cleanup()
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opt.info(self.name + ": remove output[1]")
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opt.fold_constants()
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opt.info(self.name + ": fold constants")
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opt.infer_shapes()
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opt.info(self.name + ": shape inference")
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opt.select_outputs([0], names=["text_embeddings"]) # rename network output
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opt.info(self.name + ": remove output[0]")
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opt_onnx_graph = opt.cleanup(return_onnx=True)
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opt.info(self.name + ": finished")
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return opt_onnx_graph
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class UNet(BaseModel):
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def __init__(
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self,
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fp16=False,
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device="cuda",
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max_batch_size=16,
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min_batch_size=1,
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embedding_dim=768,
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text_maxlen=77,
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unet_dim=4,
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):
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super(UNet, self).__init__(
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fp16=fp16,
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device=device,
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max_batch_size=max_batch_size,
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min_batch_size=min_batch_size,
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embedding_dim=embedding_dim,
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text_maxlen=text_maxlen,
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)
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self.unet_dim = unet_dim
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self.name = "UNet"
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def get_input_names(self):
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return ["sample", "timestep", "encoder_hidden_states"]
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def get_output_names(self):
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return ["latent"]
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def get_dynamic_axes(self):
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return {
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"sample": {0: "2B", 2: "H", 3: "W"},
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"timestep": {0: "2B"},
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"encoder_hidden_states": {0: "2B"},
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"latent": {0: "2B", 2: "H", 3: "W"},
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}
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def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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(
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min_batch,
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max_batch,
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_,
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_,
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_,
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_,
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min_latent_height,
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max_latent_height,
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min_latent_width,
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max_latent_width,
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) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
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return {
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"sample": [
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(min_batch, self.unet_dim, min_latent_height, min_latent_width),
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(batch_size, self.unet_dim, latent_height, latent_width),
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(max_batch, self.unet_dim, max_latent_height, max_latent_width),
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],
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"timestep": [(min_batch,), (batch_size,), (max_batch,)],
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"encoder_hidden_states": [
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(min_batch, self.text_maxlen, self.embedding_dim),
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(batch_size, self.text_maxlen, self.embedding_dim),
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(max_batch, self.text_maxlen, self.embedding_dim),
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],
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}
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def get_shape_dict(self, batch_size, image_height, image_width):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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return {
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"sample": (2 * batch_size, self.unet_dim, latent_height, latent_width),
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"timestep": (2 * batch_size,),
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"encoder_hidden_states": (2 * batch_size, self.text_maxlen, self.embedding_dim),
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"latent": (2 * batch_size, 4, latent_height, latent_width),
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}
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def get_sample_input(self, batch_size, image_height, image_width):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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dtype = torch.float16 if self.fp16 else torch.float32
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return (
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torch.randn(
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2 * batch_size, self.unet_dim, latent_height, latent_width, dtype=torch.float32, device=self.device
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),
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torch.ones((2 * batch_size,), dtype=torch.float32, device=self.device),
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torch.randn(2 * batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device),
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)
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class VAE(BaseModel):
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def __init__(self, device, max_batch_size, min_batch_size=1):
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super(VAE, self).__init__(
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device=device,
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max_batch_size=max_batch_size,
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min_batch_size=min_batch_size,
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embedding_dim=None,
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)
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self.name = "VAE decoder"
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def get_input_names(self):
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return ["latent"]
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def get_output_names(self):
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return ["images"]
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def get_dynamic_axes(self):
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return {
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"latent": {0: "B", 2: "H", 3: "W"},
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"images": {0: "B", 2: "8H", 3: "8W"},
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}
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def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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(
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min_batch,
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max_batch,
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_,
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_,
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_,
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_,
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min_latent_height,
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max_latent_height,
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min_latent_width,
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max_latent_width,
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) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
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return {
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"latent": [
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(min_batch, 4, min_latent_height, min_latent_width),
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(batch_size, 4, latent_height, latent_width),
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(max_batch, 4, max_latent_height, max_latent_width),
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]
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}
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def get_shape_dict(self, batch_size, image_height, image_width):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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return {
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"latent": (batch_size, 4, latent_height, latent_width),
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"images": (batch_size, 3, image_height, image_width),
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}
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def get_sample_input(self, batch_size, image_height, image_width):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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return torch.randn(
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batch_size,
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4,
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latent_height,
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latent_width,
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dtype=torch.float32,
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device=self.device,
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)
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class VAEEncoder(BaseModel):
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def __init__(self, device, max_batch_size, min_batch_size=1):
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super(VAEEncoder, self).__init__(
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device=device,
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max_batch_size=max_batch_size,
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min_batch_size=min_batch_size,
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embedding_dim=None,
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)
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self.name = "VAE encoder"
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def get_input_names(self):
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return ["images"]
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def get_output_names(self):
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return ["latent"]
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def get_dynamic_axes(self):
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return {
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"images": {0: "B", 2: "8H", 3: "8W"},
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"latent": {0: "B", 2: "H", 3: "W"},
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}
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def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
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assert batch_size >= self.min_batch and batch_size <= self.max_batch
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min_batch = batch_size if static_batch else self.min_batch
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max_batch = batch_size if static_batch else self.max_batch
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self.check_dims(batch_size, image_height, image_width)
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(
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min_batch,
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max_batch,
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min_image_height,
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max_image_height,
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min_image_width,
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max_image_width,
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_,
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_,
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_,
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_,
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) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
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return {
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"images": [
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(min_batch, 3, min_image_height, min_image_width),
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(batch_size, 3, image_height, image_width),
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(max_batch, 3, max_image_height, max_image_width),
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],
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}
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def get_shape_dict(self, batch_size, image_height, image_width):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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return {
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"images": (batch_size, 3, image_height, image_width),
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"latent": (batch_size, 4, latent_height, latent_width),
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}
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def get_sample_input(self, batch_size, image_height, image_width):
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self.check_dims(batch_size, image_height, image_width)
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return torch.randn(
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batch_size,
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3,
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image_height,
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image_width,
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dtype=torch.float32,
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device=self.device,
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)
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