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Lazy upcasting for t5. (#2589)

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This commit is contained in:
Laurent Mazare
2024-10-30 18:08:51 +01:00
committed by GitHub
parent d232e132f6
commit 7ac0de15a9
3 changed files with 59 additions and 34 deletions
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29
candle-examples/examples/stable-diffusion-3/clip.rs
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@ -118,7 +118,7 @@ impl T5WithTokenizer {
.to_vec();
tokens.resize(self.max_position_embeddings, 0);
let input_token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
let embeddings = self.t5.forward(&input_token_ids)?;
let embeddings = self.t5.forward_dt(&input_token_ids, Some(DType::F32))?;
Ok(embeddings)
}
}
@ -144,7 +144,7 @@ impl StableDiffusion3TripleClipWithTokenizer {
candle_nn::VarBuilder::from_mmaped_safetensors(&[clip_l_file], DType::F16, device)?
};
let vb_t5 = unsafe {
candle_nn::VarBuilder::from_mmaped_safetensors(&[t5xxl_file], DType::F32, device)?
candle_nn::VarBuilder::from_mmaped_safetensors(&[t5xxl_file], DType::F16, device)?
};
let max_position_embeddings = 77usize;
let clip_l = ClipWithTokenizer::new(
@ -164,11 +164,6 @@ impl StableDiffusion3TripleClipWithTokenizer {
max_position_embeddings,
)?;
// Current T5 implementation does not support fp16, so we use fp32 VarBuilder for T5.
// This is a temporary workaround until the T5 implementation is updated to support fp16.
// Also see:
// https://github.com/huggingface/candle/issues/2480
// https://github.com/huggingface/candle/pull/2481
let t5 = T5WithTokenizer::new(vb_t5, max_position_embeddings)?;
Ok(Self {
clip_l,
@ -178,34 +173,26 @@ impl StableDiffusion3TripleClipWithTokenizer {
})
}
pub fn new(vb_fp16: candle_nn::VarBuilder, vb_fp32: candle_nn::VarBuilder) -> Result<Self> {
pub fn new(vb: candle_nn::VarBuilder) -> Result<Self> {
let max_position_embeddings = 77usize;
let clip_l = ClipWithTokenizer::new(
vb_fp16.pp("clip_l.transformer"),
vb.pp("clip_l.transformer"),
stable_diffusion::clip::Config::sdxl(),
"openai/clip-vit-large-patch14",
max_position_embeddings,
)?;
let clip_g = ClipWithTokenizer::new(
vb_fp16.pp("clip_g.transformer"),
vb.pp("clip_g.transformer"),
stable_diffusion::clip::Config::sdxl2(),
"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k",
max_position_embeddings,
)?;
let text_projection = candle_nn::linear_no_bias(
1280,
1280,
vb_fp16.pp("clip_g.transformer.text_projection"),
)?;
let text_projection =
candle_nn::linear_no_bias(1280, 1280, vb.pp("clip_g.transformer.text_projection"))?;
// Current T5 implementation does not support fp16, so we use fp32 VarBuilder for T5.
// This is a temporary workaround until the T5 implementation is updated to support fp16.
// Also see:
// https://github.com/huggingface/candle/issues/2480
// https://github.com/huggingface/candle/pull/2481
let t5 = T5WithTokenizer::new(vb_fp32.pp("t5xxl.transformer"), max_position_embeddings)?;
let t5 = T5WithTokenizer::new(vb.pp("t5xxl.transformer"), max_position_embeddings)?;
Ok(Self {
clip_l,
clip_g,

13
candle-examples/examples/stable-diffusion-3/main.rs
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@ -194,18 +194,11 @@ fn main() -> Result<()> {
api.repo(hf_hub::Repo::model(name.to_string()))
};
let model_file = sai_repo.get("sd3_medium_incl_clips_t5xxlfp16.safetensors")?;
let vb_fp16 = unsafe {
let vb = unsafe {
candle_nn::VarBuilder::from_mmaped_safetensors(&[&model_file], DType::F16, &device)?
};
let vb_fp32 = unsafe {
candle_nn::VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)?
};
let triple = StableDiffusion3TripleClipWithTokenizer::new(
vb_fp16.pp("text_encoders"),
vb_fp32.pp("text_encoders"),
)?;
(MMDiTConfig::sd3_medium(), triple, vb_fp16)
let triple = StableDiffusion3TripleClipWithTokenizer::new(vb.pp("text_encoders"))?;
(MMDiTConfig::sd3_medium(), triple, vb)
};
let (context, y) = triple.encode_text_to_embedding(prompt.as_str(), &device)?;
let (context_uncond, y_uncond) =

51
candle-transformers/src/models/t5.rs
View File

@ -1,12 +1,38 @@
// T5 Text Model
// https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py
use crate::models::with_tracing::{linear_no_bias, Embedding, Linear};
use crate::models::with_tracing::Embedding;
use candle::{DType, Device, Module, Result, Tensor, D};
use candle_nn::{Activation, VarBuilder};
use serde::Deserialize;
use std::sync::Arc;
#[derive(Debug, Clone)]
pub struct Linear {
weight: Tensor,
span: tracing::Span,
}
pub fn linear_no_bias(d1: usize, d2: usize, vb: VarBuilder) -> Result<Linear> {
let init_ws = candle_nn::init::DEFAULT_KAIMING_NORMAL;
let weight = vb.get_with_hints((d2, d1), "weight", init_ws)?;
let span = tracing::span!(tracing::Level::TRACE, "linear");
Ok(Linear { weight, span })
}
impl Module for Linear {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let weight = self.weight.to_dtype(xs.dtype())?;
let w = match *xs.dims() {
[b1, b2, _, _] => weight.broadcast_left((b1, b2))?.t()?,
[bsize, _, _] => weight.broadcast_left(bsize)?.t()?,
_ => weight.t()?,
};
xs.matmul(&w)
}
}
fn default_relative_attention_max_distance() -> usize {
128
}
@ -185,7 +211,7 @@ impl Module for T5LayerNorm {
let variance = xs_f32.sqr()?.mean_keepdim(D::Minus1)?;
let xs = xs_f32.broadcast_div(&(variance + self.variance_epsilon)?.sqrt()?)?;
let xs = xs.to_dtype(dtype)?;
let xs = xs.broadcast_mul(&self.weight)?;
let xs = xs.broadcast_mul(&self.weight.to_dtype(dtype)?)?;
Ok(xs)
}
}
@ -472,7 +498,8 @@ impl T5Attention {
let position_bias = relative_attention_bias
.forward(&relative_buckets)?
.permute((2, 0, 1))?
.unsqueeze(0)?;
.unsqueeze(0)?
.to_dtype(scores.dtype())?;
(scores.broadcast_add(&position_bias)?, Some(position_bias))
// TODO: position_bias_masked?
}
@ -678,9 +705,22 @@ impl T5Stack {
&mut self,
input_ids: &Tensor,
encoder_hidden_states: Option<&Tensor>,
) -> Result<Tensor> {
self.forward_dt(input_ids, encoder_hidden_states, None)
}
fn forward_dt(
&mut self,
input_ids: &Tensor,
encoder_hidden_states: Option<&Tensor>,
dtype: Option<DType>,
) -> Result<Tensor> {
let _enter = self.span.enter();
let input_embeds = self.shared.as_ref().forward(input_ids)?;
let input_embeds = match dtype {
None => input_embeds,
Some(dtype) => input_embeds.to_dtype(dtype)?,
};
let mut hidden_states = input_embeds;
let mut position_bias = None;
for block in self.block.iter_mut() {
@ -729,6 +769,11 @@ impl T5EncoderModel {
self.encoder.forward(input_ids, None)
}
pub fn forward_dt(&mut self, input_ids: &Tensor, dtype: Option<DType>) -> Result<Tensor> {
let _enter = self.span.enter();
self.encoder.forward_dt(input_ids, None, dtype)
}
pub fn device(&self) -> &Device {
&self.device
}