import gradio as gr
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import datasets
import asyncio
import numpy as np
def make_script(shader_code):
# code copied and fixed(escaping single quotes to double quotes!!!) from https://webglfundamentals.org/webgl/webgl-shadertoy.html
script = ("""
WebGL - Shadertoy
▶
\nblank canvas here indicates that some of the shadertoy specific functions are not yet supported with this implementation (like #define I believe). you can always copy and paste the code into a shadertoy.com window to try.
""")
return script
def make_iframe(shader_code): #keep a single function?
script = make_script(shader_code)
return f""""""
intro_text = """
# Welcome to the interactive shadercoding demo.
This gives you access to a filtered version of the [Shadertoys](https://huggingface.co/datasets/Vipitis/Shadertoys) dataset, only shaders that consist of a single pass are available.
And then lets you use code generation models to make alterations to part of the shadercode.
## How To Use:
1. Load any Model for [`text-generation`](https://huggingface.co/models?pipeline_tag=text-generation) and hit ENTER.
2. Use the slider to sample a shader from the dataset.
- The original shader will be embedding on the left, click on title to get to the source.
- The shadercode will be displayed on the right, this is interactive.
- A preview of the currently displayed shadercode will be displayed on the lower left. (hover to advance time)
3. use the dropdown to select a function to modify.
4. press either button to make modifications to that function
5. you can also edit the code manually.
"""
outro_text ="""
## Models to try (look at [ShaderEval](https://huggingface.co/spaces/Vipitis/ShaderEval) for an indication of how helpful they will be):
- [gpt2](https://huggingface.co/gpt2) baseline for language models, really struggles with shadercode.
- [bigscience/bloom-1b1](https://huggingface.co/bigscience/bloom-1b1) a newer and larger freely available model. Does understand a big of code.
- [codeparrot/codeparrot-small](https://huggingface.co/codeparrot/codeparrot-small) a model trained on code, but not on shadercode. Manages to graps the patterns.
- [salesforce/codegen-2B-multi](https://huggingface.co/salesforce/codegen-2B-multi) a larger model that indicates some potential.
- [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) a model trained on subset of [TheStack](https://huggingface.co/datasets/bigcode/the-stack), struggles with shadercode.
- [Vipitis/santacoder-finetuned-the-stack-glsl](https://huggingface.co/Vipitis/santacoder-finetuned-the-stack-glsl) fine-tuned by me on the glsl subset of [TheStack](https://huggingface.co/datasets/bigcode/the-stack), is an improvement.
- [Vipitis/santacoder-finetuned-Shadertoys](https://huggingface.co/Vipitis/santacoder-finetuned-Shadertoys) fine-tuned by me on whole shaders from [Shadertoys](https://huggingface.co/datasets/Vipitis/Shadertoys). Does overfit quite a bit with greedy decoding.
- [Vipitis/santacoder-finetuned-Shadertoys-fine](https://huggingface.co/Vipitis/santacoder-finetuned-Shadertoys-fine) fine-tuned by me just functions from [Shadertoys-fine](https://huggingface.co/datasets/Vipitis/Shadertoys-fine). Memorizes the exact function about half the time.
- [bigcode/starcoder](https://huggingface.co/bigcode/starcoder) a very large model which I haven't tried yet.
- **any other model you want to**
## TODO (feel free to contribute with a [Pull-Request](https://huggingface.co/Vipitis/santacoder-finetuned-the-stack-glsl/discussions?status=open&type=pull_request)):
- [x] use embedded Shadertoy for reference/attribution (done, but some errors)
- [~] working render implementation on CPU only space (as webgl via webglfundamentals, ccs needs fixing for iframe (or hijack Shadertoy iframe))
- [~] generate variations of return statements [ShaderEval task1](https://huggingface.co/spaces/Vipitis/ShaderEval) (needs to be reworked using the other parts)
- [x] generate whole functions (seems to work quite well)
- [] dropdown for model selection (from curated list or all supported models?)
- [] generation history stating which function and orig/generated returns. (use State ??). do it as comments in the code?
- [~] display errros/issues to the user (raise gr.Error could be one idea, but highlighting in the code would be awesome) currently adds a comment to the code.
- [] generate whole shaders (via prompts guidance, recursive from errors)
- [] accordion with generation parameters (as pipeline_kwargs?) look up starcoder playround and take "inspiration" from there
- [] support FIM task for better model context
- [~] include some context for prompt (title, comments before a functions) - now works with the first comment inside a function body (has to be first)
- [] gradio examples
### Notes:
- this is meant as a resource to show code generation for a "creative" task.
- the goal is not to not replace shader artists, but aims to be an assistant instead.
- the space still lacks quite a lot of features, but will continue to evolve.
- this demo can be useful to sannity check evaluation results, where the academic numbers are made.
- If you create a remix with these tools, please attribute the original creator of your starting point when sharing the results. (And perhaps share in the [discussion tab](https://huggingface.co/Vipitis/santacoder-finetuned-the-stack-glsl/discussions?status=open&type=discussion) too)
"""
passes_dataset = datasets.load_dataset("Vipitis/Shadertoys")
single_passes = passes_dataset.filter(lambda x: not x["has_inputs"] and x["num_passes"] == 1) #could also include shaders with no extra functions.
all_single_passes = datasets.concatenate_datasets([single_passes["train"], single_passes["test"]])
num_samples = len(all_single_passes)
import tree_sitter
from tree_sitter import Language, Parser
Language.build_library("./build/my-languages.so", ['tree-sitter-glsl'])
GLSL_LANGUAGE = Language('./build/my-languages.so', 'glsl')
parser = Parser()
parser.set_language(GLSL_LANGUAGE)
def grab_sample(sample_idx):
sample_pass = all_single_passes[sample_idx]
sample_code = sample_pass["code"]
sample_source = sample_pass["source"]
sample_title = sample_pass["title"]
sample_auhtor = sample_pass["author"]
source_iframe = construct_embed(sample_source)
print(f"{source_iframe=}")
# sample_funcs = _parse_functions(sample_code)
# funcs = _parse_functions(sample_code)
# func_identifiers = [f"{idx:2d}: {n.child_by_field_name('declarator').text.decode()}" for idx, n in enumerate(funcs)]
# print(f"updating drop down to:{func_identifiers}")
return sample_pass, sample_code, source_iframe, funcs#, gr.Dropdown.update(choices=func_identifiers) #, sample_title, sample_auhtor
def _parse_functions(in_code):
"""
returns all functions in the code as their actual nodes.
includes any comment made directly after the function definition or diretly after #copilot trigger
"""
tree = parser.parse(bytes(in_code, "utf8"))
funcs = [n for n in tree.root_node.children if n.type == "function_definition"]
return funcs
PIPE = None
def _make_pipeline(model_cp = "Vipitis/santacoder-finetuned-Shadertoys-fine"): #bad default model for testing
tokenizer = AutoTokenizer.from_pretrained(model_cp, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_cp, trust_remote_code=True)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, trust_remote_code=True)
PIPE = pipe # set the global?
print(f"loaded model {model_cp} as a pipline")
return pipe
def process_retn(retn):
return retn.split(";")[0].strip()
def get_full_replacement(orig_code, retn_start_idx, retn_end_idx, prediction) -> str:
"""
Batches the generated return statement into the code and returns the full altered code.
"""
print(f"{orig_code[retn_start_idx:retn_end_idx]=}")
generated = process_retn(prediction)
print(f"{generated=}")
variation = orig_code[:retn_start_idx] + generated + orig_code[retn_end_idx:]
return variation
def alter_return(orig_code, func_idx="0:", pipeline=PIPE): #default pipeline can't be passed as gloabl?
"""
Replaces the return statement of a function with a generated one.
Args:
orig_code (str): The original code.
func_idx (int): The index of the function to replace the return statement of.
pipeline (Pipeline): The pipeline to use for generation.
Returns:
str: The altered code.
"""
if pipeline is None:
print("no pipeline found, loading default one")
pipeline = _make_pipeline()
print(f"{func_idx=}")
func_idx = int(func_idx.split(":")[0].strip())
retrns = []
retrn_start_idx = orig_code.find("return")
while retrn_start_idx != -1:
retrn_end_idx = orig_code.find(";", retrn_start_idx)
retrns.append((retrn_start_idx, retrn_end_idx))
retrn_start_idx = orig_code.find("return", retrn_end_idx)
num_returns = len(retrns)
if num_returns == 0:
print("no return statement found, returning original code")
return orig_code
func_idx = int(max(0, min(func_idx, num_returns - 1))) #clamp to valid range, cast to int as a bodge.
retrn_start_idx, retrn_end_idx = retrns[func_idx]
model_context = orig_code[:retrn_start_idx] #TODO: maximal context?
model_inp = model_context + "return"
new_toks = (retrn_end_idx - retrn_start_idx) * 2 #TODO: approximation, we do have early stopping? maybe also use a number instead?
pipe_generation = pipeline(model_inp, max_new_tokens=new_toks, return_full_text=False)[0]["generated_text"] #pipeline kwargs are missing?!
altered_code = get_full_replacement(orig_code, retrn_start_idx+7, retrn_end_idx, pipe_generation)
return altered_code
def _line_chr2char(text, line_idx, chr_idx):
"""
returns the character index at the given line and character index.
"""
lines = text.split("\n")
char_idx = 0
for i in range(line_idx):
char_idx += len(lines[i]) + 1
char_idx += chr_idx
return char_idx
def alter_body(old_code, func_id: str, funcs_list: list, pipeline=PIPE):
"""
Replaces the body of a function with a generated one.
Args:
old_code (str): The original code.
func_node (Node): The node of the function to replace the body of.
pipeline (Pipeline): The pipeline to use for generation.
Returns:
str: The altered code.
"""
print(f"{func_id=}")
func_id = int(func_id.split(":")[0].strip()) #undo their string casting?
func_node = funcs_list[func_id]
print(f"using for generation: {func_node=}")
print(f"{pipeline=}") # check if default even loaded
if pipeline is None:
print("no pipeline found, loading default one")
pipeline = _make_pipeline("Vipitis/santacoder-finetuned-Shadertoys-fine")
func_start_idx = _line_chr2char(old_code, func_node.start_point[0], func_node.start_point[1])
identifier_str = func_node.child_by_field_name("type").text.decode() + " " + func_node.child_by_field_name("declarator").text.decode() #func_start_idx:body_start_idx?
body_node = func_node.child_by_field_name("body")
body_start_idx = _line_chr2char(old_code, body_node.start_point[0], body_node.start_point[1])
body_end_idx = _line_chr2char(old_code, body_node.end_point[0], body_node.end_point[1])
print(f"{old_code[body_start_idx:body_end_idx]=}")
model_context = identifier_str # base case
# add any comments at the beginning of the function to the model_context
second_child = func_node.child_by_field_name("body").children[1] #might error out?
if second_child.type == "comment":
# print(second_child.text.decode())
model_context += " { \n " + second_child.text.decode()
print(f"{model_context=}")
num_new_tokens = max(160,(body_end_idx - body_start_idx) + 10) #TODO: approximation, we do have early stopping? maybe also use a number instead? HARD MAX for performance limits.
print(f"generating up to {num_new_tokens} after {model_context!r}")
generation = pipeline(model_context, max_new_tokens=num_new_tokens, return_full_text=False)[0]["generated_text"]
print(f"{generation=}")
ctx_with_generation = model_context + generation
print(f"{ctx_with_generation=}")
try:
#strip the body
first_gened_func = _parse_functions(ctx_with_generation)[0] # truncate generation to a single function?
except IndexError:
print("generation wasn't a full function.")
altered_code = old_code[:func_start_idx] + model_context + generation + "//the generation didn't complete the function!\n" + old_code[body_end_idx:] #needs a newline to break out of the comment.
return altered_code, pipeline
# raise gr.Error(f"didn't generate a full function: {generation!r}]")
print(f"{first_gened_func=}")
generated_body = first_gened_func.child_by_field_name("body").text.decode()
print(f"{generated_body=}")
altered_code = old_code[:func_start_idx] + identifier_str + generated_body + old_code[body_end_idx:]
return altered_code, pipeline
def add_history(func_id, orig_rtn, gened_rtn, history):
# is this a list? or a JSON dict?
history[func_id] = (orig_rtn, gened_rtn)
return history, history
def list_dropdown(in_code): #only used for auto update, not on sample pick?
funcs = _parse_functions(in_code)
# print(f"updating drop down to:{func_identifiers=}")
func_identifiers = [f"{idx:2d}: {n.child_by_field_name('declarator').text.decode()}" for idx, n in enumerate(funcs)]
# funcs = [n for n in funcs] #wrapped as set to avoid json issues?
print(f"updating drop down to:{func_identifiers}")
return funcs, gr.Dropdown.update(choices=func_identifiers)
def construct_embed(source_url):
shader_id = source_url.split("/")[-1]
return f''
with gr.Blocks() as site:
top_md = gr.Markdown(intro_text)
model_cp = gr.Textbox(value="Vipitis/santacoder-finetuned-Shadertoys-fine", label="Model Checkpoint (Enter to load!)", interactive=True)
sample_idx = gr.Slider(minimum=0, maximum=num_samples, value=3211, label="pick sample from dataset", step=1.0)
func_dropdown = gr.Dropdown(label="chose a function to modify") #breaks if I add a string in before that?
with gr.Row():
gen_return_button = gr.Button("generate a alternate return statement", label="generate return")
gen_func_button = gr.Button("generate an alternate function body", label="generate function")
# update_funcs_button = gr.Button("update functions", label="update functions")
with gr.Row():
with gr.Column():
source_embed = gr.HTML('', label="How this shader originally renders")
our_embed = gr.HTML(label="glsl render of the current code")
sample_code = gr.Code("// touch the slider to select a shader", label="Current Code (will update changes you generate)", language=None)
bot_md = gr.Markdown(outro_text)
sample_pass = gr.State(value={})
pipe = gr.State(value=PIPE)
pipe.value=_make_pipeline("Vipitis/santacoder-finetuned-Shadertoys-fine") # set a default like this?
funcs = gr.State(value=[])
# hist_state = gr.State(Value={})
# history_table = gr.JSON()
model_cp.submit(fn=_make_pipeline, inputs=[model_cp], outputs=[pipe]) # how can we trigger this on load?
sample_idx.release(fn=grab_sample, inputs=[sample_idx], outputs=[sample_pass, sample_code, source_embed])
gen_return_button.click(fn=alter_return, inputs=[sample_code, func_dropdown, pipe], outputs=[sample_code])
gen_func_button.click(fn=alter_body, inputs=[sample_code, func_dropdown, funcs, pipe], outputs=[sample_code, pipe])
sample_code.change(fn=list_dropdown, inputs=[sample_code], outputs=[funcs, func_dropdown]) # to update this after generation, so spans aren't messed up
sample_code.change(fn=make_iframe, inputs=[sample_code], outputs=[our_embed]) #twice could cause issues, find better ways.
site.launch()