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Added flux demo #3418
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431cc4d
Added CPU offloading
cehongwang 8168f92
Chagned CPU offload to default
cehongwang 23ca669
Added support to module with graph break
cehongwang 953f339
Added back the control flag and fixed the CI
cehongwang 797c670
Chagned CPU offload to default
cehongwang 024992d
Added flux demo
cehongwang 5b4beab
changed the file place and deleted unnecessary code
cehongwang c9573a1
Fixed memory overhead and enabled Flux with Mutable Module
cehongwang 2e90e73
Supported LoRA
cehongwang ef5bca8
Refined Flux demo, solved a bug of device mismatch, and prototyped Cu…
cehongwang a34d25c
Enabled Cuda Graph
cehongwang b8fafae
Enabled weight streaming and CudaGraph. Supported MTTM saving with dy…
cehongwang b6a96d8
Changed the Refitting test to disable CPU offload
cehongwang 53d06f3
Fixed Cuda Error
cehongwang 51c3a90
Fixed the bug of SDXL Cuda Error
cehongwang 3920a63
Changed the way to enable CudaGraph for MTTM
cehongwang 0cb1dc2
Finalize the refit revision
cehongwang 6066d51
Fixed the comments
cehongwang d23853d
Correct the flux export example
cehongwang b7b433a
Added a textbox to display time the generation process takes
cehongwang 7d2e1c3
Added perf script
cehongwang b941b75
added back control flag
cehongwang 13bd604
trying to add quantization to Flux
cehongwang e6e817a
Enable int8 and fp8 quantization for FLUX
cehongwang 41f1f80
Optimized FLUX compilation memory usage
cehongwang 1346fd4
Optimized lowering and decomposition to benchmark quantization again
cehongwang 084724e
Fixed the benchmark typo
cehongwang fb373a0
Use MutableTorchTensorRTModule to do quantization
cehongwang c67ee2f
Added quantization debug script
cehongwang 9c7edb2
Fixed fp16 quantization error
cehongwang f536ac6
Added converter registration
cehongwang File filter
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import argparse | ||
import re | ||
import time | ||
|
||
import gradio as gr | ||
import modelopt.torch.quantization as mtq | ||
import register_sdpa | ||
import torch | ||
import torch_tensorrt | ||
from diffusers import FluxPipeline | ||
|
||
parser = argparse.ArgumentParser( | ||
description="Run Flux quantization with different dtypes" | ||
) | ||
|
||
parser.add_argument( | ||
"--dtype", | ||
choices=["fp8", "int8", "fp16"], | ||
default="fp16", | ||
help="Select the data type to use (fp8 or int8 or fp16)", | ||
) | ||
args = parser.parse_args() | ||
# Update enabled precisions based on dtype argument | ||
|
||
if args.dtype == "fp8": | ||
enabled_precisions = {torch.float8_e4m3fn, torch.float16} | ||
ptq_config = mtq.FP8_DEFAULT_CFG | ||
elif args.dtype == "int8": | ||
enabled_precisions = {torch.int8, torch.float16} | ||
ptq_config = mtq.INT8_DEFAULT_CFG | ||
ptq_config["quant_cfg"]["*weight_quantizer"]["axis"] = None | ||
elif args.dtype == "fp16": | ||
enabled_precisions = {torch.float16} | ||
print(f"\nUsing {args.dtype}") | ||
|
||
|
||
DEVICE = "cuda:0" | ||
pipe = FluxPipeline.from_pretrained( | ||
"black-forest-labs/FLUX.1-dev", | ||
torch_dtype=torch.float16, | ||
) | ||
|
||
|
||
pipe.to(DEVICE).to(torch.float16) | ||
backbone = pipe.transformer | ||
backbone.eval() | ||
|
||
|
||
def filter_func(name): | ||
pattern = re.compile( | ||
r".*(time_emb_proj|time_embedding|conv_in|conv_out|conv_shortcut|add_embedding|pos_embed|time_text_embed|context_embedder|norm_out|x_embedder).*" | ||
) | ||
return pattern.match(name) is not None | ||
|
||
|
||
def do_calibrate( | ||
pipe, | ||
prompt: str, | ||
) -> None: | ||
""" | ||
Run calibration steps on the pipeline using the given prompts. | ||
""" | ||
image = pipe( | ||
prompt, | ||
output_type="pil", | ||
num_inference_steps=20, | ||
generator=torch.Generator("cuda").manual_seed(0), | ||
).images[0] | ||
|
||
|
||
def forward_loop(mod): | ||
# Switch the pipeline's backbone, run calibration | ||
pipe.transformer = mod | ||
do_calibrate( | ||
pipe=pipe, | ||
prompt="test", | ||
) | ||
|
||
|
||
if args.dtype != "fp16": | ||
backbone = mtq.quantize(backbone, ptq_config, forward_loop) | ||
mtq.disable_quantizer(backbone, filter_func) | ||
|
||
batch_size = 2 | ||
|
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BATCH = torch.export.Dim("batch", min=1, max=8) | ||
dynamic_shapes = { | ||
"hidden_states": {0: BATCH}, | ||
"encoder_hidden_states": {0: BATCH}, | ||
"pooled_projections": {0: BATCH}, | ||
"timestep": {0: BATCH}, | ||
"txt_ids": {}, | ||
"img_ids": {}, | ||
"guidance": {0: BATCH}, | ||
"joint_attention_kwargs": {}, | ||
"return_dict": None, | ||
} | ||
|
||
settings = { | ||
"strict": False, | ||
"allow_complex_guards_as_runtime_asserts": True, | ||
"enabled_precisions": enabled_precisions, | ||
"truncate_double": True, | ||
"min_block_size": 1, | ||
"debug": False, | ||
"use_python_runtime": True, | ||
"immutable_weights": False, | ||
"offload_module_to_cpu": True, | ||
} | ||
|
||
trt_gm = torch_tensorrt.MutableTorchTensorRTModule(backbone, **settings) | ||
trt_gm.set_expected_dynamic_shape_range((), dynamic_shapes) | ||
pipe.transformer = trt_gm | ||
|
||
|
||
def generate_image(prompt, inference_step, batch_size=2): | ||
start_time = time.time() | ||
image = pipe( | ||
prompt, | ||
output_type="pil", | ||
num_inference_steps=inference_step, | ||
num_images_per_prompt=batch_size, | ||
).images | ||
end_time = time.time() | ||
return image, end_time - start_time | ||
|
||
|
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generate_image(["Test"], 2) | ||
torch.cuda.empty_cache() | ||
|
||
|
||
def model_change(model): | ||
if model == "Torch Model": | ||
pipe.transformer = backbone | ||
backbone.to(DEVICE) | ||
else: | ||
backbone.to("cpu") | ||
pipe.transformer = trt_gm | ||
torch.cuda.empty_cache() | ||
|
||
|
||
def load_lora(path): | ||
|
||
pipe.load_lora_weights( | ||
path, | ||
adapter_name="lora1", | ||
) | ||
pipe.set_adapters(["lora1"], adapter_weights=[1]) | ||
pipe.fuse_lora() | ||
pipe.unload_lora_weights() | ||
print("LoRA loaded! Begin refitting") | ||
generate_image(["Test"], 2) | ||
print("Refitting Finished!") | ||
|
||
|
||
# Create Gradio interface | ||
with gr.Blocks(title="Flux Demo with Torch-TensorRT") as demo: | ||
gr.Markdown("# Flux Image Generation Demo Accelerated by Torch-TensorRT") | ||
|
||
with gr.Row(): | ||
with gr.Column(): | ||
# Input components | ||
prompt_input = gr.Textbox( | ||
label="Prompt", placeholder="Enter your prompt here...", lines=3 | ||
) | ||
model_dropdown = gr.Dropdown( | ||
choices=["Torch Model", "Torch-TensorRT Accelerated Model"], | ||
value="Torch-TensorRT Accelerated Model", | ||
label="Model Variant", | ||
) | ||
|
||
lora_upload_path = gr.Textbox( | ||
label="LoRA Path", | ||
placeholder="Enter the LoRA checkpoint path here", | ||
value="/home/TensorRT/examples/apps/NGRVNG.safetensors", | ||
lines=2, | ||
) | ||
num_steps = gr.Slider( | ||
minimum=20, maximum=100, value=20, step=1, label="Inference Steps" | ||
) | ||
batch_size = gr.Slider( | ||
minimum=1, maximum=8, value=1, step=1, label="Batch Size" | ||
) | ||
|
||
generate_btn = gr.Button("Generate Image") | ||
load_lora_btn = gr.Button("Load LoRA") | ||
|
||
with gr.Column(): | ||
# Output component | ||
output_image = gr.Gallery(label="Generated Image") | ||
time_taken = gr.Textbox( | ||
label="Generation Time (seconds)", interactive=False | ||
) | ||
|
||
# Connect the button to the generation function | ||
model_dropdown.change(model_change, inputs=[model_dropdown]) | ||
load_lora_btn.click( | ||
fn=load_lora, | ||
inputs=[ | ||
lora_upload_path, | ||
], | ||
) | ||
|
||
# Update generate button click to include time output | ||
generate_btn.click( | ||
fn=generate_image, | ||
inputs=[ | ||
prompt_input, | ||
num_steps, | ||
batch_size, | ||
], | ||
outputs=[output_image, time_taken], | ||
) | ||
|
||
# Launch the interface | ||
if __name__ == "__main__": | ||
demo.launch() |
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# %% | ||
# Import the following libraries | ||
# ----------------------------- | ||
import re | ||
|
||
import modelopt.torch.opt as mto | ||
import modelopt.torch.quantization as mtq | ||
import torch | ||
import torch_tensorrt | ||
from diffusers import FluxPipeline | ||
from diffusers.models.attention_processor import Attention | ||
from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel | ||
from modelopt.torch.quantization.utils import export_torch_mode | ||
from torch.export._trace import _export | ||
from transformers import AutoModelForCausalLM | ||
|
||
# %% | ||
DEVICE = "cuda:0" | ||
pipe = FluxPipeline.from_pretrained( | ||
"black-forest-labs/FLUX.1-dev", | ||
torch_dtype=torch.float32, | ||
) | ||
pipe.transformer = FluxTransformer2DModel( | ||
num_layers=1, num_single_layers=1, guidance_embeds=True | ||
) | ||
|
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pipe.to(DEVICE).to(torch.float32) | ||
# Store the config and transformer backbone | ||
config = pipe.transformer.config | ||
# global backbone | ||
backbone = pipe.transformer | ||
backbone.eval() | ||
|
||
|
||
def filter_func(name): | ||
pattern = re.compile( | ||
r".*(time_emb_proj|time_embedding|conv_in|conv_out|conv_shortcut|add_embedding|pos_embed|time_text_embed|context_embedder|norm_out|x_embedder).*" | ||
) | ||
return pattern.match(name) is not None | ||
|
||
|
||
def generate_image(pipe, prompt, image_name): | ||
seed = 42 | ||
image = pipe( | ||
prompt, | ||
output_type="pil", | ||
num_inference_steps=20, | ||
generator=torch.Generator("cuda").manual_seed(seed), | ||
).images[0] | ||
image.save(f"{image_name}.png") | ||
print(f"Image generated using {image_name} model saved as {image_name}.png") | ||
|
||
|
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generate_image(pipe, ["A golden retriever holding a sign to code"], "dog_code") | ||
|
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# %% | ||
# Quantization | ||
|
||
|
||
def do_calibrate( | ||
pipe, | ||
prompt: str, | ||
) -> None: | ||
""" | ||
Run calibration steps on the pipeline using the given prompts. | ||
""" | ||
image = pipe( | ||
prompt, | ||
output_type="pil", | ||
num_inference_steps=20, | ||
generator=torch.Generator("cuda").manual_seed(0), | ||
).images[0] | ||
|
||
|
||
def forward_loop(mod): | ||
# Switch the pipeline's backbone, run calibration | ||
pipe.transformer = mod | ||
do_calibrate( | ||
pipe=pipe, | ||
prompt="test", | ||
) | ||
|
||
|
||
ptq_config = mtq.FP8_DEFAULT_CFG | ||
backbone = mtq.quantize(backbone, ptq_config, forward_loop) | ||
mtq.disable_quantizer(backbone, filter_func) | ||
|
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# %% | ||
# Export the backbone using torch.export | ||
# -------------------------------------------------- | ||
# Define the dummy inputs and their respective dynamic shapes. We export the transformer backbone with dynamic shapes with a ``batch_size=2`` | ||
# due to `0/1 specialization <https://docs.google.com/document/d/16VPOa3d-Liikf48teAOmxLc92rgvJdfosIy-yoT38Io/edit?fbclid=IwAR3HNwmmexcitV0pbZm_x1a4ykdXZ9th_eJWK-3hBtVgKnrkmemz6Pm5jRQ&tab=t.0#heading=h.ez923tomjvyk>`_ | ||
|
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batch_size = 2 | ||
BATCH = torch.export.Dim("batch", min=1, max=2) | ||
SEQ_LEN = torch.export.Dim("seq_len", min=1, max=512) | ||
# This particular min, max values for img_id input are recommended by torch dynamo during the export of the model. | ||
# To see this recommendation, you can try exporting using min=1, max=4096 | ||
IMG_ID = torch.export.Dim("img_id", min=3586, max=4096) | ||
dynamic_shapes = { | ||
"hidden_states": {0: BATCH}, | ||
"encoder_hidden_states": {0: BATCH, 1: SEQ_LEN}, | ||
"pooled_projections": {0: BATCH}, | ||
"timestep": {0: BATCH}, | ||
"txt_ids": {0: SEQ_LEN}, | ||
"img_ids": {0: IMG_ID}, | ||
"guidance": {0: BATCH}, | ||
"joint_attention_kwargs": {}, | ||
"return_dict": None, | ||
} | ||
# The guidance factor is of type torch.float32 | ||
dummy_inputs = { | ||
"hidden_states": torch.randn((batch_size, 4096, 64), dtype=torch.float32).to( | ||
DEVICE | ||
), | ||
"encoder_hidden_states": torch.randn( | ||
(batch_size, 512, 4096), dtype=torch.float32 | ||
).to(DEVICE), | ||
"pooled_projections": torch.randn((batch_size, 768), dtype=torch.float32).to( | ||
DEVICE | ||
), | ||
"timestep": torch.tensor([1.0, 1.0], dtype=torch.float32).to(DEVICE), | ||
"txt_ids": torch.randn((512, 3), dtype=torch.float32).to(DEVICE), | ||
"img_ids": torch.randn((4096, 3), dtype=torch.float32).to(DEVICE), | ||
"guidance": torch.tensor([1.0, 1.0], dtype=torch.float32).to(DEVICE), | ||
"joint_attention_kwargs": {}, | ||
"return_dict": False, | ||
} | ||
|
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# This will create an exported program which is going to be compiled with Torch-TensorRT | ||
with export_torch_mode(): | ||
ep = _export( | ||
backbone, | ||
args=(), | ||
kwargs=dummy_inputs, | ||
dynamic_shapes=dynamic_shapes, | ||
strict=False, | ||
allow_complex_guards_as_runtime_asserts=True, | ||
) | ||
|
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with torch_tensorrt.logging.debug(): | ||
trt_gm = torch_tensorrt.dynamo.compile( | ||
ep, | ||
inputs=dummy_inputs, | ||
enabled_precisions={torch.float8_e4m3fn}, | ||
truncate_double=True, | ||
min_block_size=1, | ||
debug=False, | ||
use_python_runtime=True, | ||
immutable_weights=True, | ||
offload_module_to_cpu=True, | ||
) | ||
|
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del ep | ||
pipe.transformer = trt_gm | ||
pipe.transformer.config = config | ||
|
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# %% | ||
trt_gm.device = torch.device(DEVICE) | ||
# Function which generates images from the flux pipeline | ||
|
||
for _ in range(2): | ||
generate_image(pipe, ["A golden retriever holding a sign to code"], "dog_code") | ||
|
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# For this dummy model, the fp16 engine size is around 1GB, fp32 engine size is around 2GB |
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