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fix import paths

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This commit is contained in:
SolitaryThinker
2025-03-14 23:13:34 +00:00
parent d5ac1e9bee
commit d2db0d475b
11 changed files with 39 additions and 343 deletions
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fastvideo/v1/distributed/device_communicators/pynccl.py
+1 -1
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@@ -13,7 +13,7 @@ from fastvideo.v1.distributed.device_communicators.pynccl_wrapper import (
ncclRedOpTypeEnum, ncclUniqueId)
from fastvideo.v1.distributed.utils import StatelessProcessGroup
from fastvideo.v1.logger import init_logger
from fastvideo.v1.utils.utils import current_stream
from fastvideo.v1.utils import current_stream
logger = init_logger(__name__)
fastvideo/v1/distributed/device_communicators/pynccl_wrapper.py
+1 -1
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@@ -31,7 +31,7 @@ import torch
from torch.distributed import ReduceOp
from fastvideo.v1.logger import init_logger
from fastvideo.v1.utils.utils import find_nccl_library
from fastvideo.v1.utils import find_nccl_library
logger = init_logger(__name__)
fastvideo/v1/inference_args.py
+1 -1
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@@ -18,7 +18,7 @@
import argparse
import dataclasses
from fastvideo.v1.logger import init_logger
from fastvideo.v1.utils.utils import FlexibleArgumentParser
from fastvideo.v1.utils import FlexibleArgumentParser
from typing import List, Optional
fastvideo/v1/inference_engine.py
+5 -9
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@@ -7,20 +7,16 @@ This module provides classes and functions for running inference with diffusion
import os
import time
import torch
import numpy as np
from typing import Any, Dict, List, Optional, Union, Type
from PIL import Image
import uuid
import random
from typing import Any, Dict
from fastvideo.v1.inference_args import InferenceArgs
from fastvideo.v1.pipelines import ComposedPipelineBase, build_pipeline
from fastvideo.v1.pipelines.pipeline_registry import PipelineRegistry
from fastvideo.v1.pipelines.pipeline_batch_info import ForwardBatch
from fastvideo.v1.logger import init_logger
# TODO(will): remove
from fastvideo.v1.models.hunyuan.utils.data_utils import align_to
from fastvideo.v1.models.hunyuan.constants import NEGATIVE_PROMPT
# TODO(will): remove, check if this is hunyuan specific
from fastvideo.v1.utils import align_to
# TODO(will): remove, move this to hunyuan stage
from fastvideo.v1.pipelines.implementations.hunyuan.constants import NEGATIVE_PROMPT
logger = init_logger(__name__)
fastvideo/v1/models/dits/hunyuanvideo.py
+2 -2
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@@ -7,8 +7,8 @@ from fastvideo.v1.layers.layernorm import LayerNormScaleShift, ScaleResidual, Sc
from fastvideo.v1.layers.visual_embedding import PatchEmbed, TimestepEmbedder, ModulateProjection, unpatchify
from fastvideo.v1.layers.rotary_embedding import _apply_rotary_emb, get_rotary_pos_embed
from fastvideo.v1.distributed.parallel_state import get_sequence_model_parallel_world_size, get_sequence_model_parallel_rank
# TODO: RMSNorm ....
from fastvideo.v1.models.hunyuan.modules.norm_layers import RMSNorm
# TODO(will-PY-refactor): RMSNorm ....
from fastvideo.v1.v0_reference_src.models.hunyuan.modules.norm_layers import RMSNorm
from fastvideo.v1.layers.mlp import MLP
from fastvideo.v1.models.dits.base import BaseDiT
fastvideo/v1/models/encoders/encoder.py
-323
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@@ -1,323 +0,0 @@
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
import torch.nn as nn
from transformers import AutoModel, AutoTokenizer, CLIPTextModel, CLIPTokenizer
from transformers.utils import ModelOutput
from ..constants import PRECISION_TO_TYPE, TEXT_ENCODER_PATH, TOKENIZER_PATH
def use_default(value, default):
return value if value is not None else default
def load_text_encoder(
text_encoder_type,
text_encoder_precision=None,
text_encoder_path=None,
logger=None,
device=None,
):
if text_encoder_path is None:
text_encoder_path = TEXT_ENCODER_PATH[text_encoder_type]
if logger is not None:
logger.info(f"Loading text encoder model ({text_encoder_type}) from: {text_encoder_path}")
if text_encoder_type == "clipL":
text_encoder = CLIPTextModel.from_pretrained(text_encoder_path)
text_encoder.final_layer_norm = text_encoder.text_model.final_layer_norm
elif text_encoder_type == "llm":
text_encoder = AutoModel.from_pretrained(text_encoder_path, low_cpu_mem_usage=True)
text_encoder.final_layer_norm = text_encoder.norm
else:
raise ValueError(f"Unsupported text encoder type: {text_encoder_type}")
# from_pretrained will ensure that the model is in eval mode.
if text_encoder_precision is not None:
text_encoder = text_encoder.to(dtype=PRECISION_TO_TYPE[text_encoder_precision])
text_encoder.requires_grad_(False)
if logger is not None:
logger.info(f"Text encoder to dtype: {text_encoder.dtype}")
if device is not None:
text_encoder = text_encoder.to(device)
return text_encoder, text_encoder_path
def load_tokenizer(tokenizer_type, tokenizer_path=None, padding_side="right", logger=None):
if tokenizer_path is None:
tokenizer_path = TOKENIZER_PATH[tokenizer_type]
if logger is not None:
logger.info(f"Loading tokenizer ({tokenizer_type}) from: {tokenizer_path}")
if tokenizer_type == "clipL":
tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path, max_length=77)
elif tokenizer_type == "llm":
tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, padding_side=padding_side)
else:
raise ValueError(f"Unsupported tokenizer type: {tokenizer_type}")
return tokenizer, tokenizer_path
@dataclass
class TextEncoderModelOutput(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
hidden_states_list (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
text_outputs (`list`, *optional*, returned when `return_texts=True` is passed):
List of decoded texts.
"""
hidden_state: torch.FloatTensor = None
attention_mask: Optional[torch.LongTensor] = None
hidden_states_list: Optional[Tuple[torch.FloatTensor, ...]] = None
text_outputs: Optional[list] = None
class TextEncoder(nn.Module):
def __init__(
self,
text_encoder_type: str,
max_length: int,
text_encoder_precision: Optional[str] = None,
text_encoder_path: Optional[str] = None,
tokenizer_type: Optional[str] = None,
tokenizer_path: Optional[str] = None,
output_key: Optional[str] = None,
use_attention_mask: bool = True,
input_max_length: Optional[int] = None,
prompt_template: Optional[dict] = None,
prompt_template_video: Optional[dict] = None,
hidden_state_skip_layer: Optional[int] = None,
apply_final_norm: bool = False,
reproduce: bool = False,
logger=None,
device=None,
):
super().__init__()
self.text_encoder_type = text_encoder_type
self.max_length = max_length
self.precision = text_encoder_precision
self.model_path = text_encoder_path
self.tokenizer_type = (tokenizer_type if tokenizer_type is not None else text_encoder_type)
self.tokenizer_path = (tokenizer_path if tokenizer_path is not None else text_encoder_path)
self.use_attention_mask = use_attention_mask
if prompt_template_video is not None:
assert (use_attention_mask is True), "Attention mask is True required when training videos."
self.input_max_length = (input_max_length if input_max_length is not None else max_length)
self.prompt_template = prompt_template
self.prompt_template_video = prompt_template_video
self.hidden_state_skip_layer = hidden_state_skip_layer
self.apply_final_norm = apply_final_norm
self.reproduce = reproduce
self.logger = logger
self.use_template = self.prompt_template is not None
if self.use_template:
assert (isinstance(self.prompt_template, dict) and "template" in self.prompt_template
), f"`prompt_template` must be a dictionary with a key 'template', got {self.prompt_template}"
assert "{}" in str(self.prompt_template["template"]), (
"`prompt_template['template']` must contain a placeholder `{}` for the input text, "
f"got {self.prompt_template['template']}")
self.use_video_template = self.prompt_template_video is not None
if self.use_video_template:
if self.prompt_template_video is not None:
assert (
isinstance(self.prompt_template_video, dict) and "template" in self.prompt_template_video
), f"`prompt_template_video` must be a dictionary with a key 'template', got {self.prompt_template_video}"
assert "{}" in str(self.prompt_template_video["template"]), (
"`prompt_template_video['template']` must contain a placeholder `{}` for the input text, "
f"got {self.prompt_template_video['template']}")
if "t5" in text_encoder_type:
self.output_key = output_key or "last_hidden_state"
elif "clip" in text_encoder_type:
self.output_key = output_key or "pooler_output"
elif "llm" in text_encoder_type or "glm" in text_encoder_type:
self.output_key = output_key or "last_hidden_state"
else:
raise ValueError(f"Unsupported text encoder type: {text_encoder_type}")
self.model, self.model_path = load_text_encoder(
text_encoder_type=self.text_encoder_type,
text_encoder_precision=self.precision,
text_encoder_path=self.model_path,
logger=self.logger,
device=device,
)
self.dtype = self.model.dtype
self.device = self.model.device
self.tokenizer, self.tokenizer_path = load_tokenizer(
tokenizer_type=self.tokenizer_type,
tokenizer_path=self.tokenizer_path,
padding_side="right",
logger=self.logger,
)
def __repr__(self):
return f"{self.text_encoder_type} ({self.precision} - {self.model_path})"
@staticmethod
def apply_text_to_template(text, template, prevent_empty_text=True):
"""
Apply text to template.
Args:
text (str): Input text.
template (str or list): Template string or list of chat conversation.
prevent_empty_text (bool): If True, we will prevent the user text from being empty
by adding a space. Defaults to True.
"""
if isinstance(template, str):
# Will send string to tokenizer. Used for llm
return template.format(text)
else:
raise TypeError(f"Unsupported template type: {type(template)}")
def text2tokens(self, text, data_type="image"):
"""
Tokenize the input text.
Args:
text (str or list): Input text.
"""
tokenize_input_type = "str"
if self.use_template:
if data_type == "image":
prompt_template = self.prompt_template["template"]
elif data_type == "video":
prompt_template = self.prompt_template_video["template"]
else:
raise ValueError(f"Unsupported data type: {data_type}")
if isinstance(text, (list, tuple)):
text = [self.apply_text_to_template(one_text, prompt_template) for one_text in text]
if isinstance(text[0], list):
tokenize_input_type = "list"
elif isinstance(text, str):
text = self.apply_text_to_template(text, prompt_template)
if isinstance(text, list):
tokenize_input_type = "list"
else:
raise TypeError(f"Unsupported text type: {type(text)}")
kwargs = dict(
truncation=True,
max_length=self.max_length,
padding="max_length",
return_tensors="pt",
)
if tokenize_input_type == "str":
return self.tokenizer(
text,
return_length=False,
return_overflowing_tokens=False,
return_attention_mask=True,
**kwargs,
)
elif tokenize_input_type == "list":
return self.tokenizer.apply_chat_template(
text,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
**kwargs,
)
else:
raise ValueError(f"Unsupported tokenize_input_type: {tokenize_input_type}")
def encode(
self,
batch_encoding,
use_attention_mask=None,
output_hidden_states=False,
do_sample=None,
hidden_state_skip_layer=None,
return_texts=False,
data_type="image",
device=None,
):
"""
Args:
batch_encoding (dict): Batch encoding from tokenizer.
use_attention_mask (bool): Whether to use attention mask. If None, use self.use_attention_mask.
Defaults to None.
output_hidden_states (bool): Whether to output hidden states. If False, return the value of
self.output_key. If True, return the entire output. If set self.hidden_state_skip_layer,
output_hidden_states will be set True. Defaults to False.
do_sample (bool): Whether to sample from the model. Used for Decoder-Only LLMs. Defaults to None.
When self.produce is False, do_sample is set to True by default.
hidden_state_skip_layer (int): Number of hidden states to hidden_state_skip_layer. 0 means the last layer.
If None, self.output_key will be used. Defaults to None.
return_texts (bool): Whether to return the decoded texts. Defaults to False.
"""
device = self.model.device if device is None else device
use_attention_mask = use_default(use_attention_mask, self.use_attention_mask)
hidden_state_skip_layer = use_default(hidden_state_skip_layer, self.hidden_state_skip_layer)
do_sample = use_default(do_sample, not self.reproduce)
attention_mask = (batch_encoding["attention_mask"].to(device) if use_attention_mask else None)
outputs = self.model(
input_ids=batch_encoding["input_ids"].to(device),
attention_mask=attention_mask,
output_hidden_states=output_hidden_states or hidden_state_skip_layer is not None,
)
if hidden_state_skip_layer is not None:
last_hidden_state = outputs.hidden_states[-(hidden_state_skip_layer + 1)]
# Real last hidden state already has layer norm applied. So here we only apply it
# for intermediate layers.
if hidden_state_skip_layer > 0 and self.apply_final_norm:
last_hidden_state = self.model.final_layer_norm(last_hidden_state)
else:
last_hidden_state = outputs[self.output_key]
# Remove hidden states of instruction tokens, only keep prompt tokens.
if self.use_template:
if data_type == "image":
crop_start = self.prompt_template.get("crop_start", -1)
elif data_type == "video":
crop_start = self.prompt_template_video.get("crop_start", -1)
else:
raise ValueError(f"Unsupported data type: {data_type}")
if crop_start > 0:
last_hidden_state = last_hidden_state[:, crop_start:]
attention_mask = (attention_mask[:, crop_start:] if use_attention_mask else None)
if output_hidden_states:
return TextEncoderModelOutput(last_hidden_state, attention_mask, outputs.hidden_states)
return TextEncoderModelOutput(last_hidden_state, attention_mask)
def forward(
self,
text,
use_attention_mask=None,
output_hidden_states=False,
do_sample=False,
hidden_state_skip_layer=None,
return_texts=False,
):
batch_encoding = self.text2tokens(text)
return self.encode(
batch_encoding,
use_attention_mask=use_attention_mask,
output_hidden_states=output_hidden_states,
do_sample=do_sample,
hidden_state_skip_layer=hidden_state_skip_layer,
return_texts=return_texts,
)
fastvideo/v1/models/loader.py
+1 -1
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@@ -156,7 +156,7 @@ class VAELoader(ComponentLoader):
def load(self, model_path: str, architecture: str, inference_args: InferenceArgs):
"""Load the VAE based on the model path, architecture, and inference args."""
# TODO(will): move this to a constants file
from fastvideo.v1.pipelines.hunyuan.constants import PRECISION_TO_TYPE
from fastvideo.v1.utils import PRECISION_TO_TYPE
config = get_diffusers_config(model=model_path)
fastvideo/v1/pipelines/stages/decoding.py
+1 -1
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@@ -6,7 +6,7 @@ import torch
from fastvideo.v1.pipelines.stages.base import PipelineStage
from fastvideo.v1.pipelines.pipeline_batch_info import ForwardBatch
from fastvideo.v1.inference_args import InferenceArgs
from fastvideo.v1.models.hunyuan.constants import PRECISION_TO_TYPE
from fastvideo.v1.utils import PRECISION_TO_TYPE
from fastvideo.v1.logger import init_logger
logger = init_logger(__name__)
fastvideo/v1/pipelines/stages/denoising.py
+4 -3
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@@ -11,9 +11,10 @@ from einops import rearrange
from fastvideo.v1.pipelines.stages.base import PipelineStage
from fastvideo.v1.pipelines.pipeline_batch_info import ForwardBatch
from fastvideo.v1.inference_args import InferenceArgs
from fastvideo.v1.models.hunyuan.constants import PRECISION_TO_TYPE
from fastvideo.v1.utils.parallel_states import nccl_info, get_sequence_parallel_state
from fastvideo.v1.utils.communications import all_gather
from fastvideo.v1.utils import PRECISION_TO_TYPE
# TODO(will-refactor): change this to fastvideo.distributed
from fastvideo.utils.parallel_states import nccl_info, get_sequence_parallel_state
from fastvideo.utils.communications import all_gather
from fastvideo.v1.logger import init_logger
logger = init_logger(__name__)
fastvideo/v1/sample/v1_fastvideo_inference.py
+1 -1
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@@ -11,7 +11,7 @@ from einops import rearrange
# Fix the import path
from fastvideo.v1.inference_engine import InferenceEngine
from fastvideo.v1.inference_args import InferenceArgs
from fastvideo.v1.utils.utils import FlexibleArgumentParser
from fastvideo.v1.utils import FlexibleArgumentParser
from fastvideo.v1.distributed import init_distributed_environment, initialize_model_parallel
from fastvideo.v1.logger import init_logger
logger = init_logger(__name__)
fastvideo/v1/utils/utils.py → fastvideo/v1/utils.py
+22
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@@ -3,6 +3,7 @@ import fastvideo.v1.envs as envs
import inspect
from fastvideo.v1.logger import init_logger
import argparse
import math
import sys
from typing import List, Dict, Union, Type, Any, TypeVar
import yaml
@@ -13,6 +14,14 @@ logger = init_logger(__name__)
T = TypeVar("T")
# TODO(will): used to convert inference_args.precision to torch.dtype. Find a
# cleaner way to do this.
PRECISION_TO_TYPE = {
"fp32": torch.float32,
"fp16": torch.float16,
"bf16": torch.bfloat16,
}
def find_nccl_library() -> str:
"""
@@ -288,3 +297,16 @@ def warn_for_unimplemented_methods(cls: Type[T]) -> Type[T]:
type.__setattr__(cls, '__init__', wrapped_init)
return cls
def align_to(value, alignment):
"""align height, width according to alignment
Args:
value (int): height or width
alignment (int): target alignment factor
Returns:
int: the aligned value
"""
return int(math.ceil(value / alignment) * alignment)