tensorrt_llm.models.dit.model — TensorRT-LLM (original) (raw)

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import math from collections import OrderedDict

import numpy as np import tensorrt as trt

from ..._utils import str_dtype_to_trt, trt_dtype_to_str from ...functional import (Tensor, allgather, 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 ...quantization import QuantMode 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=np.float32)).cast(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,
             quant_mode=QuantMode(0)):
    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,
                              cp_group=mapping.cp_group,
                              cp_size=mapping.cp_size,
                              cp_rank=mapping.cp_rank,
                              dtype=dtype,
                              quant_mode=quant_mode)
    self.norm2 = LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
    self.mlp = MLP(hidden_size=hidden_size,
                   ffn_hidden_size=int(hidden_size * mlp_ratio),
                   hidden_act='gelu',
                   tp_group=mapping.tp_group,
                   tp_size=mapping.tp_size,
                   dtype=dtype,
                   quant_mode=quant_mode)
    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

[docs] 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.mapping = config.mapping

    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,
                 quant_mode=config.quant_mode)
        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)

# We need to invoke default `__post_init__()` for quantized layers.
#  def __post_init__(self):
#     return

[docs] 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)

[docs] 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

[docs] 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

[docs] 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)) # Split squeence for CP here if self.mapping.cp_size > 1: assert x.shape[1] % self.mapping.cp_size == 0 x = chunk(x, self.mapping.cp_size, dim=1)[self.mapping.cp_rank] input_lengths = input_lengths // self.mapping.cp_size 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)

    # All gather after CP
    if self.mapping.cp_size > 1:
        x = allgather(x, self.mapping.cp_group, gather_dim=1)
    x = self.unpatchify(x)  # (N, out_channels, H, W)
    self.register_network_output('unpatchify', x)
    return x

[docs] 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)

[docs] 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(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}