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# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from collections import OrderedDict
import numpy as np
import tensorrt as trt
from ..._utils import str_dtype_to_trt, trt_dtype_to_np, trt_dtype_to_str
from ...functional import (Tensor, arange, chunk, concat, constant, cos, exp,
expand, shape, silu, sin, slice, split, unsqueeze)
from ...layers import MLP, BertAttention, Conv2d, Embedding, LayerNorm, Linear
from ...mapping import Mapping
from ...module import Module, ModuleList
from ...parameter import Parameter
from ...plugin import current_all_reduce_helper
from ..modeling_utils import PretrainedConfig, PretrainedModel
def modulate(x, shift, scale, dtype):
ones = 1.0
if dtype is not None:
ones = constant(np.ones(1, dtype=trt_dtype_to_np(dtype)))
return x * (ones + unsqueeze(scale, 1)) + unsqueeze(shift, 1)
class TimestepEmbedder(Module):
def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None):
super().__init__()
self.dtype = dtype
self.mlp1 = Linear(frequency_embedding_size,
hidden_size,
bias=True,
dtype=dtype)
self.mlp2 = Linear(hidden_size, hidden_size, bias=True, dtype=dtype)
self.frequency_embedding_size = frequency_embedding_size
def timestep_embedding(self, t, dim, max_period=10000):
half = dim // 2
freqs = exp(
-math.log(max_period) *
arange(start=0, end=half, dtype=trt_dtype_to_str(trt.float32)) /
constant(np.array([half], dtype=np.float32)))
args = unsqueeze(t, -1).cast(trt.float32) * unsqueeze(freqs, 0)
embedding = concat([cos(args), sin(args)], dim=-1)
if self.dtype is not None: embedding = embedding.cast(self.dtype)
assert dim % 2 == 0
return embedding
def forward(self, t):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
t_emb = self.mlp2(silu(self.mlp1(t_freq)))
return t_emb
class LabelEmbedder(Module):
def __init__(self, num_classes, hidden_size, dropout_prob, dtype=None):
super().__init__()
use_cfg_embedding = dropout_prob > 0
self.embedding_table = Embedding(num_classes + use_cfg_embedding,
hidden_size,
dtype=dtype)
self.num_classes = num_classes
self.dropout_prob = dropout_prob
def forward(self, labels, force_drop_ids=None):
assert force_drop_ids is None
embeddings = self.embedding_table(labels)
return embeddings
class PatchEmbed(Module):
def __init__(self,
img_size: int,
patch_size: int,
input_c: int,
output_c: int,
bias: bool = True,
dtype: trt.DataType = None):
super().__init__()
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = (img_size // patch_size)**2
self.proj = Conv2d(input_c,
output_c,
kernel_size=(patch_size, patch_size),
stride=(patch_size, patch_size),
bias=bias,
dtype=dtype)
def forward(self, x):
assert x.shape[2] == self.img_size
assert x.shape[3] == self.img_size
x = self.proj(x)
x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
return x
class DiTBlock(Module):
def __init__(self,
hidden_size,
num_heads,
mapping=Mapping(),
mlp_ratio=4.0,
dtype=None):
super().__init__()
self.dtype = dtype
self.norm1 = LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = BertAttention(hidden_size,
num_heads,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
tp_rank=mapping.tp_rank,
dtype=dtype)
self.norm2 = LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.mlp = MLP(hidden_size=hidden_size,
ffn_hidden_size=hidden_size * mlp_ratio,
hidden_act='gelu',
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
dtype=dtype)
self.adaLN_modulation = Linear(hidden_size,
6 * hidden_size,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
bias=True,
dtype=dtype)
def forward(self, x, c, input_lengths):
c = self.adaLN_modulation(silu(c))
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = chunk(
c, 6, dim=1)
x = x + unsqueeze(gate_msa, 1) * self.attn(modulate(
self.norm1(x), shift_msa, scale_msa, self.dtype),
input_lengths=input_lengths)
x = x + unsqueeze(gate_mlp, 1) * self.mlp(
modulate(self.norm2(x), shift_mlp, scale_mlp, self.dtype))
return x
class FinalLayer(Module):
def __init__(self,
hidden_size,
patch_size,
out_channels,
mapping=Mapping(),
dtype=None):
super().__init__()
self.dtype = dtype
self.norm_final = LayerNorm(hidden_size,
elementwise_affine=False,
eps=1e-6)
self.linear = Linear(hidden_size,
patch_size * patch_size * out_channels,
bias=True,
dtype=dtype)
self.adaLN_modulation = Linear(hidden_size,
2 * hidden_size,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
bias=True,
dtype=dtype)
def forward(self, x, c):
shift, scale = chunk(self.adaLN_modulation(silu(c)), 2, dim=1)
x = modulate(self.norm_final(x), shift, scale, self.dtype)
x = self.linear(x)
return x
class DiT(PretrainedModel):
def __init__(self, config: PretrainedConfig):
self.check_config(config)
super().__init__(config)
self.learn_sigma = config.learn_sigma
self.in_channels = config.in_channels
self.out_channels = config.in_channels * 2 if config.learn_sigma else config.in_channels
self.input_size = config.input_size
self.patch_size = config.patch_size
self.num_heads = config.num_attention_heads
self.dtype = str_dtype_to_trt(config.dtype)
self.cfg_scale = config.cfg_scale
self.x_embedder = PatchEmbed(config.input_size,
config.patch_size,
config.in_channels,
config.hidden_size,
bias=True,
dtype=self.dtype)
self.t_embedder = TimestepEmbedder(config.hidden_size, dtype=self.dtype)
self.y_embedder = LabelEmbedder(config.num_classes,
config.hidden_size,
config.class_dropout_prob,
dtype=self.dtype)
num_patches = self.x_embedder.num_patches
self.pos_embed = Parameter(shape=(1, num_patches, config.hidden_size),
dtype=self.dtype)
self.blocks = ModuleList([
DiTBlock(config.hidden_size,
config.num_attention_heads,
mlp_ratio=config.mlp_ratio,
mapping=config.mapping,
dtype=self.dtype) for _ in range(config.num_hidden_layers)
])
self.final_layer = FinalLayer(config.hidden_size,
config.patch_size,
self.out_channels,
mapping=config.mapping,
dtype=self.dtype)
def __post_init__(self):
return
def check_config(self, config: PretrainedConfig):
config.set_if_not_exist('input_size', 32)
config.set_if_not_exist('patch_size', 2)
config.set_if_not_exist('in_channels', 4)
config.set_if_not_exist('mlp_ratio', 4.0)
config.set_if_not_exist('class_dropout_prob', 0.1)
config.set_if_not_exist('num_classes', 1000)
config.set_if_not_exist('learn_sigma', True)
config.set_if_not_exist('dtype', None)
config.set_if_not_exist('cfg_scale', None)
def unpatchify(self, x: Tensor):
c = self.out_channels
p = self.x_embedder.patch_size
h = w = int(x.shape[1]**0.5)
assert h * w == x.shape[1]
x = x.view(shape=(x.shape[0], h, w, p, p, c))
x = x.permute((0, 5, 1, 3, 2, 4))
imgs = x.view(shape=(x.shape[0], c, h * p, h * p))
return imgs
def forward(self, latent, timestep, label):
"""
Forward pass of DiT.
latent: (N, C, H, W)
timestep: (N,)
label: (N,)
"""
if self.cfg_scale is not None:
output = self.forward_with_cfg(latent, timestep, label)
else:
output = self.forward_without_cfg(latent, timestep, label)
output.mark_output('output', self.dtype)
return output
def forward_without_cfg(self, x, t, y):
"""
Forward pass without classifier-free guidance.
"""
x = self.x_embedder(x) + self.pos_embed.value
t = self.t_embedder(t)
y = self.y_embedder(y)
self.register_network_output('t_embedder', t)
self.register_network_output('x_embedder', x)
self.register_network_output('y_embedder', y)
c = t + y
input_length = constant(np.array([x.shape[1]], dtype=np.int32))
input_lengths = expand(input_length, unsqueeze(shape(x, 0), 0))
for block in self.blocks:
x = block(x, c, input_lengths) # (N, T, D)
self.register_network_output('before_final_layer', x)
x = self.final_layer(x, c) # (N, T, patch_size ** 2 * out_channels)
self.register_network_output('final_layer', x)
x = self.unpatchify(x) # (N, out_channels, H, W)
self.register_network_output('unpatchify', x)
return x
def forward_with_cfg(self, x, t, y):
"""
Forward pass with classifier-free guidance.
"""
batch_size = shape(x, 0)
half = slice(
x, [0, 0, 0, 0],
concat([batch_size / 2, x.shape[1], x.shape[2], x.shape[3]]))
combined = concat([half, half], dim=0)
self.register_network_output('combined', combined)
model_out = self.forward_without_cfg(combined, t, y)
_, d, h, w = model_out.shape
eps, rest = split(model_out, [3, d - 3], dim=1)
cond_eps = slice(eps, [0, 0, 0, 0], concat([batch_size / 2, 3, h, w]))
uncond_eps = slice(eps, concat([batch_size / 2, 0, 0, 0]),
concat([batch_size / 2, 3, h, w]))
self.register_network_output('cond_eps', cond_eps)
self.register_network_output('uncond_eps', uncond_eps)
half_eps = uncond_eps + self.cfg_scale * (cond_eps - uncond_eps)
eps = concat([half_eps, half_eps], dim=0)
self.register_network_output('eps', eps)
return concat([eps, rest], dim=1)
def prepare_inputs(self, max_batch_size, **kwargs):
'''@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the
ranges of the dimensions of when using TRT dynamic shapes.
@return: a list contains values which can be fed into the self.forward()
'''
mapping = self.config.mapping
if mapping.tp_size > 1:
current_all_reduce_helper().set_workspace_tensor(mapping, 1)
def dit_default_range(max_batch_size):
return [2, (max_batch_size + 1) // 2, max_batch_size]
default_range = dit_default_range
if self.cfg_scale is not None:
max_batch_size *= 2
latent = Tensor(
name='latent',
dtype=self.dtype,
shape=[-1, self.in_channels, self.input_size, self.input_size],
dim_range=OrderedDict([
('batch_size', [default_range(max_batch_size)]),
('in_channels', [[self.in_channels] * 3]),
('latent_height', [[self.input_size] * 3]),
('latent_width', [[self.input_size] * 3]),
]))
timestep = Tensor(name='timestep',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([
('batch_size', [default_range(max_batch_size)]),
]))
label = Tensor(name='label',
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([
('batch_size', [default_range(max_batch_size)]),
]))
return {'latent': latent, 'timestep': timestep, 'label': label}
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