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Fix batch count and tweaks to chatbot. (#2151)

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* Fix batch count

* Add button to unload models manually.

* Add compiled pipeline option

* Add brevitas to requirements

* Tweaks to chatbot

* Change script loading trigger
This commit is contained in:
Ean Garvey
2024-05-31 08:18:28 -05:00
committed by GitHub
parent 4505c4549f
commit d2c3752dc7
6 changed files with 99 additions and 47 deletions
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3
.github/workflows/test-studio.yml vendored
View File

@@ -81,4 +81,5 @@ jobs:
source shark.venv/bin/activate
pip install -r requirements.txt --no-cache-dir
pip install -e .
python apps/shark_studio/tests/api_test.py
# Disabled due to hang when exporting test llama2
# python apps/shark_studio/tests/api_test.py

38
apps/shark_studio/api/llm.py
View File

@@ -3,8 +3,13 @@ from turbine_models.model_runner import vmfbRunner
from turbine_models.gen_external_params.gen_external_params import gen_external_params
import time
from shark.iree_utils.compile_utils import compile_module_to_flatbuffer
from apps.shark_studio.web.utils.file_utils import get_resource_path
from apps.shark_studio.web.utils.file_utils import (
get_resource_path,
get_checkpoints_path,
)
from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
from apps.shark_studio.api.utils import parse_device
from urllib.request import urlopen
import iree.runtime as ireert
from itertools import chain
import gc
@@ -65,6 +70,7 @@ class LanguageModel:
use_system_prompt=True,
streaming_llm=False,
):
_, _, self.triple = parse_device(device)
self.hf_model_name = llm_model_map[model_name]["hf_model_name"]
self.device = device.split("=>")[-1].strip()
self.backend = self.device.split("://")[0]
@@ -165,6 +171,7 @@ class LanguageModel:
precision=self.precision,
quantization=self.quantization,
streaming_llm=self.streaming_llm,
decomp_attn=True,
)
with open(self.tempfile_name, "w+") as f:
f.write(self.torch_ir)
@@ -194,11 +201,27 @@ class LanguageModel:
)
elif self.backend == "vulkan":
flags.extend(["--iree-stream-resource-max-allocation-size=4294967296"])
elif self.backend == "rocm":
flags.extend(
[
"--iree-codegen-llvmgpu-enable-transform-dialect-jit=false",
"--iree-llvmgpu-enable-prefetch=true",
"--iree-opt-outer-dim-concat=true",
"--iree-flow-enable-aggressive-fusion",
]
)
if "gfx9" in self.triple:
flags.extend(
[
f"--iree-codegen-transform-dialect-library={get_mfma_spec_path(self.triple, get_checkpoints_path())}",
"--iree-codegen-llvmgpu-use-vector-distribution=true",
]
)
flags.extend(llm_model_map[self.hf_model_name]["compile_flags"])
flatbuffer_blob = compile_module_to_flatbuffer(
self.tempfile_name,
device=self.device,
frontend="torch",
frontend="auto",
model_config_path=None,
extra_args=flags,
write_to=self.vmfb_name,
@@ -329,6 +352,17 @@ class LanguageModel:
return result_output, total_time
def get_mfma_spec_path(target_chip, save_dir):
url = "https://raw.githubusercontent.com/iree-org/iree/main/build_tools/pkgci/external_test_suite/attention_and_matmul_spec.mlir"
attn_spec = urlopen(url).read().decode("utf-8")
spec_path = os.path.join(save_dir, "attention_and_matmul_spec_mfma.mlir")
if os.path.exists(spec_path):
return spec_path
with open(spec_path, "w") as f:
f.write(attn_spec)
return spec_path
def llm_chat_api(InputData: dict):
from datetime import datetime as dt

49
apps/shark_studio/api/sd.py
View File

@@ -104,7 +104,7 @@ class StableDiffusion:
self.base_model_id = base_model_id
self.custom_vae = custom_vae
self.is_sdxl = "xl" in self.base_model_id.lower()
self.is_custom = "custom" in self.base_model_id.lower()
self.is_custom = ".py" in self.base_model_id.lower()
if self.is_custom:
custom_module = load_script(
os.path.join(get_checkpoints_path("scripts"), self.base_model_id),
@@ -112,8 +112,7 @@ class StableDiffusion:
)
self.turbine_pipe = custom_module.StudioPipeline
self.model_map = custom_module.MODEL_MAP
if self.is_sdxl:
elif self.is_sdxl:
self.turbine_pipe = SharkSDXLPipeline
self.model_map = EMPTY_SDXL_MAP
else:
@@ -181,12 +180,17 @@ class StableDiffusion:
print(f"\n[LOG] Pipeline initialized with pipe_id: {self.pipe_id}.")
gc.collect()
def prepare_pipe(self, custom_weights, adapters, embeddings, is_img2img):
def prepare_pipe(
self, custom_weights, adapters, embeddings, is_img2img, compiled_pipeline
):
print(f"\n[LOG] Preparing pipeline...")
self.is_img2img = False
mlirs = copy.deepcopy(self.model_map)
vmfbs = copy.deepcopy(self.model_map)
weights = copy.deepcopy(self.model_map)
if not self.is_sdxl:
compiled_pipeline = False
self.compiled_pipeline = compiled_pipeline
if custom_weights:
custom_weights = os.path.join(
@@ -253,7 +257,6 @@ class StableDiffusion:
guidance_scale,
seed,
ondemand,
repeatable_seeds,
resample_type,
control_mode,
hints,
@@ -272,7 +275,7 @@ class StableDiffusion:
def shark_sd_fn_dict_input(
sd_kwargs: dict,
):
print("[LOG] Submitting Request...")
print("\n[LOG] Submitting Request...")
for key in sd_kwargs:
if sd_kwargs[key] in [None, []]:
@@ -282,9 +285,8 @@ def shark_sd_fn_dict_input(
if key == "seed":
sd_kwargs[key] = int(sd_kwargs[key])
for i in range(1):
generated_imgs = yield from shark_sd_fn(**sd_kwargs)
yield generated_imgs
generated_imgs = yield from shark_sd_fn(**sd_kwargs)
return generated_imgs
def shark_sd_fn(
@@ -307,7 +309,7 @@ def shark_sd_fn(
device: str,
target_triple: str,
ondemand: bool,
repeatable_seeds: bool,
compiled_pipeline: bool,
resample_type: str,
controlnets: dict,
embeddings: dict,
@@ -370,6 +372,7 @@ def shark_sd_fn(
"adapters": adapters,
"embeddings": embeddings,
"is_img2img": is_img2img,
"compiled_pipeline": compiled_pipeline,
}
submit_run_kwargs = {
"prompt": prompt,
@@ -379,7 +382,6 @@ def shark_sd_fn(
"guidance_scale": guidance_scale,
"seed": seed,
"ondemand": ondemand,
"repeatable_seeds": repeatable_seeds,
"resample_type": resample_type,
"control_mode": control_mode,
"hints": hints,
@@ -412,22 +414,35 @@ def shark_sd_fn(
for current_batch in range(batch_count):
start_time = time.time()
out_imgs = global_obj.get_sd_obj().generate_images(**submit_run_kwargs)
if not isinstance(out_imgs, list):
out_imgs = [out_imgs]
# total_time = time.time() - start_time
# text_output = f"Total image(s) generation time: {total_time:.4f}sec"
# print(f"\n[LOG] {text_output}")
# if global_obj.get_sd_status() == SD_STATE_CANCEL:
# break
# else:
save_output_img(
out_imgs[current_batch],
seed,
sd_kwargs,
)
for batch in range(batch_size):
save_output_img(
out_imgs[batch],
seed,
sd_kwargs,
)
generated_imgs.extend(out_imgs)
# TODO: make seed changes over batch counts more configurable.
submit_run_kwargs["seed"] = submit_run_kwargs["seed"] + 1
yield generated_imgs, status_label(
"Stable Diffusion", current_batch + 1, batch_count, batch_size
)
return generated_imgs, ""
return (generated_imgs, "")
def unload_sd():
print("Unloading models.")
import apps.shark_studio.web.utils.globals as global_obj
global_obj.clear_cache()
gc.collect()
def cancel_sd():

1
apps/shark_studio/web/ui/chat.py
View File

@@ -138,6 +138,7 @@ with gr.Blocks(title="Chat") as chat_element:
label="Run in streaming mode (requires recompilation)",
value=True,
interactive=False,
visible=False,
)
prompt_prefix = gr.Checkbox(
label="Add System Prompt",

54
apps/shark_studio/web/ui/sd.py
View File

@@ -19,6 +19,7 @@ from apps.shark_studio.web.utils.file_utils import (
from apps.shark_studio.api.sd import (
shark_sd_fn_dict_input,
cancel_sd,
unload_sd,
)
from apps.shark_studio.api.controlnet import (
cnet_preview,
@@ -119,7 +120,7 @@ def pull_sd_configs(
device,
target_triple,
ondemand,
repeatable_seeds,
compiled_pipeline,
resample_type,
controlnets,
embeddings,
@@ -178,7 +179,7 @@ def load_sd_cfg(sd_json: dict, load_sd_config: str):
sd_json["device"],
sd_json["target_triple"],
sd_json["ondemand"],
sd_json["repeatable_seeds"],
sd_json["compiled_pipeline"],
sd_json["resample_type"],
sd_json["controlnets"],
sd_json["embeddings"],
@@ -587,21 +588,6 @@ with gr.Blocks(title="Stable Diffusion") as sd_element:
object_fit="fit",
preview=True,
)
with gr.Row():
std_output = gr.Textbox(
value=f"{sd_model_info}\n"
f"Images will be saved at "
f"{get_generated_imgs_path()}",
lines=2,
elem_id="std_output",
show_label=True,
label="Log",
show_copy_button=True,
)
sd_element.load(
logger.read_sd_logs, None, std_output, every=1
)
sd_status = gr.Textbox(visible=False)
with gr.Row():
batch_count = gr.Slider(
1,
@@ -620,17 +606,15 @@ with gr.Blocks(title="Stable Diffusion") as sd_element:
interactive=True,
visible=True,
)
repeatable_seeds = gr.Checkbox(
cmd_opts.repeatable_seeds,
label="Use Repeatable Seeds for Batches",
compiled_pipeline = gr.Checkbox(
False,
label="Faster txt2img (SDXL only)",
)
with gr.Row():
stable_diffusion = gr.Button("Start")
random_seed = gr.Button("Randomize Seed")
random_seed.click(
lambda: -1,
inputs=[],
outputs=[seed],
unload = gr.Button("Unload Models")
unload.click(
fn=unload_sd,
queue=False,
show_progress=False,
)
@@ -701,7 +685,7 @@ with gr.Blocks(title="Stable Diffusion") as sd_element:
device,
target_triple,
ondemand,
repeatable_seeds,
compiled_pipeline,
resample_type,
cnet_config,
embeddings_config,
@@ -718,6 +702,22 @@ with gr.Blocks(title="Stable Diffusion") as sd_element:
inputs=[sd_json, sd_config_name],
outputs=[sd_config_name],
)
with gr.Tab(label="Log", id=103) as sd_tab_log:
with gr.Row():
std_output = gr.Textbox(
value=f"{sd_model_info}\n"
f"Images will be saved at "
f"{get_generated_imgs_path()}",
lines=2,
elem_id="std_output",
show_label=True,
label="Log",
show_copy_button=True,
)
sd_element.load(
logger.read_sd_logs, None, std_output, every=1
)
sd_status = gr.Textbox(visible=False)
pull_kwargs = dict(
fn=pull_sd_configs,
@@ -741,7 +741,7 @@ with gr.Blocks(title="Stable Diffusion") as sd_element:
device,
target_triple,
ondemand,
repeatable_seeds,
compiled_pipeline,
resample_type,
cnet_config,
embeddings_config,

1
requirements.txt
View File

@@ -10,6 +10,7 @@ torch==2.3.0
shark-turbine @ git+https://github.com/iree-org/iree-turbine.git@main
turbine-models @ git+https://github.com/nod-ai/SHARK-Turbine.git@ean-unify-sd#subdirectory=models
diffusers @ git+https://github.com/nod-ai/diffusers@v0.24.0-release
brevitas @ git+https://github.com/Xilinx/brevitas.git@6695e8df7f6a2c7715b9ed69c4b78157376bb60b
# SHARK Runner
tqdm