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# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import os
from typing import TYPE_CHECKING
import torch
from loguru import logger
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
import ttnn
from models.tt_dit.encoders.clip.model_clip import CLIPConfig, CLIPEncoder
from models.tt_dit.encoders.t5.model_t5 import T5Config, T5Encoder
from models.tt_dit.parallel.config import EncoderParallelConfig
from models.tt_dit.parallel.manager import CCLManager
from models.tt_dit.pipelines.events import PipelineEventCallback, SectionEnd, SectionStart, null_callback
from models.tt_dit.utils import cache
from models.tt_dit.utils.tracing import Tracer
if TYPE_CHECKING:
from collections.abc import Sequence
T5_SEQUENCE_LENGTH = 512
class TextEncoder:
def __init__(
self,
*,
checkpoint_name: str,
device: ttnn.MeshDevice,
ccl_manager: CCLManager,
parallel_config: EncoderParallelConfig,
enable_t5: bool,
joint_attention_dim: int,
use_torch_clip: bool,
use_torch_t5: bool,
) -> None:
self._device = device
self._ccl_manager = ccl_manager
self._parallel_config = parallel_config
self._joint_attention_dim = joint_attention_dim
logger.info("loading torch text encoders...")
self._tokenizer_1 = CLIPTokenizer.from_pretrained(checkpoint_name, subfolder="tokenizer")
self._t5_tokenizer = T5TokenizerFast.from_pretrained(checkpoint_name, subfolder="tokenizer_2")
torch_text_encoder_1 = CLIPTextModel.from_pretrained(checkpoint_name, subfolder="text_encoder")
torch_text_encoder_1.eval()
torch_t5_text_encoder = (
T5EncoderModel.from_pretrained(checkpoint_name, subfolder="text_encoder_2") if enable_t5 else None
)
model_name = os.path.basename(checkpoint_name)
if use_torch_clip:
self._text_encoder_1 = torch_text_encoder_1
self._clip_tracer = None
else:
logger.info("creating TT-NN CLIP text encoder...")
clip_config_1 = CLIPConfig(
vocab_size=torch_text_encoder_1.config.vocab_size,
embed_dim=torch_text_encoder_1.config.hidden_size,
ff_dim=torch_text_encoder_1.config.intermediate_size,
num_heads=torch_text_encoder_1.config.num_attention_heads,
num_hidden_layers=torch_text_encoder_1.config.num_hidden_layers,
max_prompt_length=77,
layer_norm_eps=torch_text_encoder_1.config.layer_norm_eps,
attention_dropout=torch_text_encoder_1.config.attention_dropout,
hidden_act=torch_text_encoder_1.config.hidden_act,
)
self._text_encoder_1 = CLIPEncoder(
config=clip_config_1,
mesh_device=device,
ccl_manager=ccl_manager,
parallel_config=parallel_config,
eos_token_id=2,
)
self._text_encoder_1.load_torch_state_dict(torch_text_encoder_1.state_dict())
self._clip_tracer = Tracer(self._text_encoder_1.forward, device=device, prep_run=False)
if enable_t5:
if use_torch_t5:
self._t5_text_encoder = torch_t5_text_encoder
self._t5_tracer = None
else:
logger.info("creating TT-NN T5 text encoder...")
t5_config = T5Config(
vocab_size=torch_t5_text_encoder.config.vocab_size,
embed_dim=torch_t5_text_encoder.config.d_model,
ff_dim=torch_t5_text_encoder.config.d_ff,
kv_dim=torch_t5_text_encoder.config.d_kv,
num_heads=torch_t5_text_encoder.config.num_heads,
num_hidden_layers=torch_t5_text_encoder.config.num_layers,
max_prompt_length=T5_SEQUENCE_LENGTH,
layer_norm_eps=torch_t5_text_encoder.config.layer_norm_epsilon,
relative_attention_num_buckets=torch_t5_text_encoder.config.relative_attention_num_buckets,
relative_attention_max_distance=torch_t5_text_encoder.config.relative_attention_max_distance,
)
self._t5_text_encoder = T5Encoder(
config=t5_config,
mesh_device=device,
ccl_manager=ccl_manager,
parallel_config=parallel_config,
)
cache.load_model(
self._t5_text_encoder,
get_torch_state_dict=torch_t5_text_encoder.state_dict,
model_name=model_name,
subfolder="t5_text_encoder",
parallel_config=parallel_config,
mesh_shape=tuple(device.shape),
mesh_device=device,
)
self._t5_tracer = Tracer(self._t5_text_encoder.forward, device=device, prep_run=False)
else:
self._t5_text_encoder = None
self._t5_tracer = None
@torch.no_grad()
def encode_cfg(
self,
prompts: tuple[Sequence[str], Sequence[str]],
neg_prompts: tuple[Sequence[str], Sequence[str]] | None,
*,
num_images_per_prompt: int,
cfg_enabled: bool,
clip_skip: int = 0,
traced: bool = False,
on_event: PipelineEventCallback = null_callback,
) -> tuple[torch.Tensor, torch.Tensor]:
prompt_embeds, pooled_prompt_embeds = self._encode(
prompts,
num_images_per_prompt=num_images_per_prompt,
clip_skip=clip_skip,
traced=traced,
on_event=on_event,
)
if not cfg_enabled:
return prompt_embeds, pooled_prompt_embeds
assert neg_prompts is not None
negative_prompt_embeds, negative_pooled_prompt_embeds = self._encode(
neg_prompts,
num_images_per_prompt=num_images_per_prompt,
clip_skip=clip_skip,
traced=traced,
on_event=on_event,
)
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
return prompt_embeds, pooled_prompt_embeds
def _encode(
self,
prompts: tuple[Sequence[str], Sequence[str]],
*,
num_images_per_prompt: int,
clip_skip: int,
traced: bool,
on_event: PipelineEventCallback,
) -> tuple[torch.Tensor, torch.Tensor]:
prompts_1, prompts_2 = prompts
tokenizer_max_length = self._tokenizer_1.model_max_length
on_event(SectionStart("clip_encoding"))
_, pooled_prompt_embeds = _get_clip_prompt_embeds(
prompts=prompts_1,
num_images_per_prompt=num_images_per_prompt,
tokenizer=self._tokenizer_1,
text_encoder=self._text_encoder_1,
tracer=self._clip_tracer if traced else None,
sequence_length=tokenizer_max_length,
mesh_device=self._device,
clip_skip=clip_skip,
)
on_event(SectionEnd("clip_encoding"))
on_event(SectionStart("t5_encoding"))
prompt_embeds = _get_t5_prompt_embeds(
prompts=prompts_2,
text_encoder=self._t5_text_encoder,
tracer=self._t5_tracer if traced else None,
tokenizer=self._t5_tokenizer,
sequence_length=T5_SEQUENCE_LENGTH,
empty_sequence_length=T5_SEQUENCE_LENGTH,
num_images_per_prompt=num_images_per_prompt,
mesh_device=self._device,
embedding_dim=self._joint_attention_dim,
)
on_event(SectionEnd("t5_encoding"))
return prompt_embeds, pooled_prompt_embeds
# adapted from https://github.com/huggingface/diffusers/blob/v0.31.0/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py
def _get_clip_prompt_embeds(
*,
prompts: Sequence[str],
text_encoder: CLIPEncoder | CLIPTextModel,
tracer: Tracer | None = None,
tokenizer: CLIPTokenizer,
sequence_length: int,
num_images_per_prompt: int,
clip_skip: int = 0,
mesh_device: ttnn.MeshDevice | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
tokens = tokenizer(
prompts,
return_tensors="pt",
padding="max_length",
max_length=sequence_length,
truncation=True,
).input_ids
untruncated_tokens = tokenizer(
prompts,
return_tensors="pt",
padding="longest",
).input_ids
if untruncated_tokens.shape[-1] >= tokens.shape[-1] and not torch.equal(tokens, untruncated_tokens):
logger.warning("CLIP input text was truncated")
if isinstance(text_encoder, CLIPEncoder):
assert mesh_device is not None
tt_tokens = ttnn.from_torch(
tokens,
dtype=ttnn.uint32,
layout=ttnn.TILE_LAYOUT,
device=mesh_device,
mesh_mapper=ttnn.replicate_tensor_to_mesh_mapper(mesh_device),
)
tt_prompt_embeds, tt_normalized = (tracer or text_encoder.forward)(
prompt_tokenized=tt_tokens,
skip_pooling=True,
)
tt_pooled_prompt_embeds = text_encoder.pooled_output(
prompt_tokenized=tt_tokens,
normalized_final_state=tt_normalized,
)
tt_prompt_embeds = tt_prompt_embeds[-(clip_skip + 2)]
prompt_embeds = ttnn.to_torch(ttnn.get_device_tensors(tt_prompt_embeds)[0])
pooled_prompt_embeds = ttnn.to_torch(ttnn.get_device_tensors(tt_pooled_prompt_embeds)[0])
else:
tokens = tokens.to(device=text_encoder.device)
with torch.no_grad():
output = text_encoder.forward(tokens, output_hidden_states=True)
prompt_embeds = output.hidden_states[-(clip_skip + 2)].to("cpu")
pooled_prompt_embeds = output.pooler_output.to("cpu")
# In diffusers v0.35.1 `pooled_prompt_embeds` is repeated along the wrong dimension in
# `StableDiffusion3Pipeline`, effectively mixing up the prompts.
pooled_prompt_embeds = pooled_prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
return prompt_embeds, pooled_prompt_embeds
# adapted from https://github.com/huggingface/diffusers/blob/v0.31.0/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py
def _get_t5_prompt_embeds(
*,
prompts: Sequence[str],
text_encoder: T5Encoder | T5EncoderModel | None,
tracer: Tracer | None = None,
tokenizer: T5TokenizerFast,
sequence_length: int,
empty_sequence_length: int,
num_images_per_prompt: int,
mesh_device: ttnn.MeshDevice | None = None,
embedding_dim: int,
) -> torch.Tensor:
if text_encoder is None:
return torch.zeros([len(prompts) * num_images_per_prompt, empty_sequence_length, embedding_dim])
tokens = tokenizer(
prompts,
return_tensors="pt",
padding="max_length",
max_length=sequence_length,
truncation=True,
).input_ids
untruncated_tokens = tokenizer(
prompts,
return_tensors="pt",
padding="longest",
).input_ids
if untruncated_tokens.shape[-1] >= tokens.shape[-1] and not torch.equal(tokens, untruncated_tokens):
logger.warning("T5 input text was truncated")
if isinstance(text_encoder, T5Encoder):
assert mesh_device is not None
tt_tokens = ttnn.from_torch(
tokens,
layout=ttnn.TILE_LAYOUT,
dtype=ttnn.uint32,
device=mesh_device,
mesh_mapper=ttnn.replicate_tensor_to_mesh_mapper(mesh_device),
)
tt_hidden_states = (tracer or text_encoder.forward)(prompt=tt_tokens)
tt_prompt_embeds = tt_hidden_states[-1]
prompt_embeds = ttnn.to_torch(ttnn.get_device_tensors(tt_prompt_embeds)[0])
else:
tokens = tokens.to(device=text_encoder.device)
with torch.no_grad():
output = text_encoder.forward(tokens)
prompt_embeds = output.last_hidden_state.to("cpu")
return prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
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