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@@ -35,3 +35,37 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 csv_datasets/anneal.csv filter=lfs diff=lfs merge=lfs -text
 csv_datasets/stable.csv filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/00af53b081e80147172e6f281c01daf5ca19ada173321438914c730370aa4267 filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/07d19b24347b043902bfdbabe7c8f797bdc86291c6cece34dab371d3edc70b15 filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/2cf51925ee86c4d428b75e491bf4d94bff9b10b6024eff162b7e2b52d546d3dc filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/425e60023c6bc7560e98738444dd4689ab81898450c48150f405f58f74076213 filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/7ca2381cfeac5c280f304e7027ffbea1b7d87474672e5d6fb16d5cd881640e04 filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/b2ec465d0fb5b5eb39d2e1e3f74041a5d2fc92d413b71aa7ea0b6fb082ccba9c filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/cb4dcd82a72472daefaca85b7580ef73a7a4eda58e0d1de22e342d4d5874ff07 filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/cbd8f9e3ec3fe2fd3047846804a455a3af27d0ae568691f0d7a7c0caecfa71bd filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/d0ed346c32fbcc9eb2f0bc7e957d188c8ae428ee3ef7291fd5aa045fc6ef4ded filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/f0d399df849e2f46fed4e1a12ab79d936715ae38cd516df2db88580d14e265b5 filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/lc0 filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/lc0.p/src_benchmark_benchmark.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/lc0.p/src_engine.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/lc0.p/src_lc0ctl_describenet.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/lc0.p/src_lc0ctl_leela2onnx.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/lc0.p/src_lc0ctl_onnx2leela.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/lc0.p/src_mcts_search.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/lc0.p/src_neural_blas_network_blas.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/lc0.p/src_neural_onnx_builder.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/lc0.p/src_neural_onnx_converter.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/lc0.p/src_selfplay_game.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/lc0.p/src_selfplay_tournament.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/liblc0_lib.so filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/liblc0_lib.so.p/src_benchmark_benchmark.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/liblc0_lib.so.p/src_engine.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/liblc0_lib.so.p/src_lc0ctl_describenet.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/liblc0_lib.so.p/src_lc0ctl_leela2onnx.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/liblc0_lib.so.p/src_lc0ctl_onnx2leela.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/liblc0_lib.so.p/src_mcts_search.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/liblc0_lib.so.p/src_neural_blas_network_blas.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/liblc0_lib.so.p/src_neural_onnx_builder.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/liblc0_lib.so.p/src_neural_onnx_converter.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/liblc0_lib.so.p/src_selfplay_game.cc.o filter=lfs diff=lfs merge=lfs -text
+chess-gpt-eval-contrastive/lc0/build/release/liblc0_lib.so.p/src_selfplay_tournament.cc.o filter=lfs diff=lfs merge=lfs -text

chess-gpt-eval-contrastive/cleanup.py

@@ -0,0 +1,16 @@
+import os
+import re
+
+directory = "../chess-mamba-vs-xformer/out/Xformer/50M/"
+
+for filename in os.listdir(directory):
+    if filename.endswith(".pt"):
+        if filename == "ckpt.pt":
+            continue
+        
+        match = re.search(r'ckpt_(\d+)(?:b|g)', filename)
+        if match:
+            number = int(match.group(1))
+            if number > 33219200:
+                file_path = os.path.join(directory, filename)
+                os.remove(file_path)

chess-gpt-eval-contrastive/dedupe.py

@@ -0,0 +1,25 @@
+import pandas as pd
+filename = 'logs/xformer_50M/ckpt_35549900g_12620818850t_pt_vs_lc0_sweep.csv'
+#filename = 'logs/11M/ckpt_1188012b_pt_vs_lc0_sweep.csv'
+#filename = 'logs/11M/Round 1/ckpt_2608480_pt_vs_lc0_sweep.csv'
+
+# Read in the CSV file
+df = pd.read_csv(filename)
+
+# Count the original total
+original_total = df.shape[0]
+
+# Filter out duplicates in the 'transcript' column
+df = df.drop_duplicates(subset='transcript')
+
+# Count the remove, and remaining rows
+removed = original_total - df.shape[0]
+remaining = df.shape[0]
+
+# Print out the results
+print("Original total rows:", original_total)
+print("Removed rows:", removed)
+print("Remaining rows:", remaining)
+
+# Write the filtered data to a new CSV file
+df.to_csv(filename, index=False)

chess-gpt-eval-contrastive/dedupem.py

@@ -0,0 +1,25 @@
+import pandas as pd
+filename = 'logs/mamba_lc0lvl0_50M/anneal/ckpt_85965050g_30731830229t_pt_vs_lc0_lvl0_sweep.csv'
+#filename = 'logs/11M/ckpt_1188012b_pt_vs_lc0_sweep.csv'
+#filename = 'logs/11M/Round 1/ckpt_2608480_pt_vs_lc0_sweep.csv'
+
+# Read in the CSV file
+df = pd.read_csv(filename)
+
+# Count the original total
+original_total = df.shape[0]
+
+# Filter out duplicates in the 'transcript' column
+df = df.drop_duplicates(subset='transcript')
+
+# Count the remove, and remaining rows
+removed = original_total - df.shape[0]
+remaining = df.shape[0]
+
+# Print out the results
+print("Original total rows:", original_total)
+print("Removed rows:", removed)
+print("Remaining rows:", remaining)
+
+# Write the filtered data to a new CSV file
+df.to_csv(filename, index=False)

chess-gpt-eval-contrastive/dedupesf.py

@@ -0,0 +1,26 @@
+import pandas as pd
+
+filename = 'logs/50M/ckpt_78450050sf_pt_vs_stockfish_sweep.csv'
+#filename = 'logs/11M/ckpt_1188012b_pt_vs_lc0_sweep.csv'
+#filename = 'logs/11M/Round 1/ckpt_2608480_pt_vs_lc0_sweep.csv'
+
+# Read in the CSV file
+df = pd.read_csv(filename)
+
+# Count the original total
+original_total = df.shape[0]
+
+# Filter out duplicates in the 'transcript' column
+df = df.drop_duplicates(subset='transcript')
+
+# Count the remove, and remaining rows
+removed = original_total - df.shape[0]
+remaining = df.shape[0]
+
+# Print out the results
+print("Original total rows:", original_total)
+print("Removed rows:", removed)
+print("Remaining rows:", remaining)
+
+# Write the filtered data to a new CSV file
+df.to_csv(filename, index=False)

chess-gpt-eval-contrastive/game.txt

@@ -0,0 +1 @@
+.e4 c5 .Nf3 e6 .d4 cxd4 .Nxd4 Nc6 .Nc3 Qc7 .Be2 a6

chess-gpt-eval-contrastive/gpt_inputs/prompt.txt

@@ -0,0 +1,3 @@
+[White "Magnus Carlsen"]
+[Black "Stockfish"]
+

chess-gpt-eval-contrastive/gpt_inputs/testing_input.txt

@@ -0,0 +1,42 @@
+1. e4 e5
+2. Nf3 Nf6
+3. Nxe5 Qe7
+4. d4 c6
+5. Nc3 d6
+6. Nf3 Nxe4
+7. Nxe4 d5
+8. Be2 dxe4
+9. Nd2 f5
+10. O-O g6
+11. c4 Na6
+12. Nb3 Be6
+13. d5 Rd8
+14. Qd4 Bg7
+15. Qxa7 cxd5
+16. cxd5 Bxd5
+17. Bxa6 bxa6
+18. Qxa6 Be5
+19. Qb5+ Kf7
+20. Rd1 Qe8
+21. Qxe8+ Rhxe8
+22. Bg5 Bf6
+23. Bxf6 Kxf6
+24. Nc5 Bxa2
+25. Rxd8 Rxd8
+26. h3 Bg8
+27. Ra6+ Ke5
+28. Ra5 Rd4
+29. Nb3+ Rd5
+30. Rxd5+ Bxd5
+31. Nd2 g5
+32. Kf1 h6
+33. Ke2 Kd4
+34. g3 h5
+35. h4 gxh4
+36. gxh4 Ba2
+37. Nf1 f4
+38. Nd2 Bf7
+39. Nf1 Bc4+
+40. Ke1 Bxf1
+41. Kxf1 Kc4
+42. Ke2 Kb3

chess-gpt-eval-contrastive/gpt_query.py

@@ -0,0 +1,265 @@
+import openai
+import tiktoken
+import json
+import os
+
+# import replicate
+
+# for hugging face inference endpoints for codellama
+import requests
+
+from typing import Optional
+
+from tenacity import (
+    retry,
+    stop_after_attempt,
+    wait_random_exponential,
+)  # for exponential backoff
+
+# system message is used in openai_request()
+system_message = """Provide the next move in the chess game. Only provide the move, no move numbers."""
+
+# dollars per 1k tokens, per openai.com/pricing
+pricing_dict = {
+    "gpt-4": 0.03,
+    "gpt-4-0301": 0.03,
+    "gpt-4-0613": 0.03,
+    "gpt-3.5-turbo": 0.0015,
+    "gpt-3.5-turbo-0301": 0.0015,
+    "gpt-3.5-turbo-0613": 0.0015,
+    "gpt-3.5-turbo-16k": 0.003,
+    "babbage": 0.0005,
+    "gpt-3.5-turbo-instruct": 0.0015,
+}
+
+MAX_TOKENS = 10
+
+completion_models = [
+    "gpt-3.5-turbo-instruct",
+    "babbage",
+    "davinci",
+]
+
+
+# tenacity is to handle anytime a request fails
+@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
+def get_gpt_response(
+    prompt: str, model: str = "gpt-4", temperature: float = 0.0
+) -> Optional[str]:
+    try:
+        messages = []
+        # system message is used in openai_request()
+        # system_message_dict = {
+        #     "role": "system",
+        #     "content": system_message,
+        # }
+        initial_message = {"role": "user", "content": prompt}
+        messages.append(initial_message)
+
+        record_messages(messages, model)
+
+        # num_tokens = count_all_tokens(model, messages)
+        # prompt_cost = get_prompt_cost(model, num_tokens)
+        # print("prompt cost in $:", prompt_cost)
+
+        if model in completion_models:
+            response = get_completions_response(model, messages, temperature)
+        elif model.startswith("gpt"):
+            response = openai_chat_completion_request(model, messages, temperature)
+        elif model.startswith("openrouter"):
+            response = openrouter_request(model, messages, temperature)
+        elif model.startswith("huggingface"):
+            response = hugging_face_request(model, messages, temperature)
+        elif model.startswith("replicate"):
+            response = replicate_request(model, messages, temperature)
+        else:
+            raise Exception("Invalid model name")
+
+        # response_cost = get_response_cost(model, count_tokens(model, response))
+        # print("response cost in $:", response_cost)
+
+        messages.append({"role": "assistant", "content": response})
+        record_messages(messages, model)
+
+        return response
+    except Exception as e:
+        print(f"Error while getting GPT response: {e}")
+        return None
+
+
+def openai_chat_completion_request(
+    model: str, messages: list[dict], temperature: float
+) -> str:
+    system_message_dict = {
+        "role": "system",
+        "content": system_message,
+    }
+    messages.append(system_message_dict)
+    completion = openai.ChatCompletion.create(
+        model=model,
+        temperature=temperature,
+        messages=messages,
+    )
+    response = completion.choices[0].message.content
+    return response
+
+
+def openrouter_request(model: str, messages: list[dict], temperature: float) -> str:
+    if temperature == 0:
+        temperature = 0.001
+
+    with open("gpt_inputs/openrouter_api_key.txt", "r") as f:
+        openai.api_key = f.read().strip()
+
+    openai.api_base = "https://openrouter.ai/api/v1"
+    OPENROUTER_REFERRER = "https://github.com/adamkarvonen/nanoGPT"
+
+    model = model.replace("openrouter/", "")
+
+    completion = openai.ChatCompletion.create(
+        model=model,
+        headers={"HTTP-Referer": OPENROUTER_REFERRER},
+        messages=messages,
+        temperature=temperature,
+        max_tokens=MAX_TOKENS,
+    )
+    response = completion.choices[0].message.content
+    return response
+
+
+def replicate_request(model: str, messages: list[dict], temperature: float) -> str:
+    if temperature == 0:
+        temperature = 0.001
+
+    with open("gpt_inputs/replicate_api_key.txt", "r") as f:
+        api_key = f.read().strip()
+    os.environ["REPLICATE_API_TOKEN"] = api_key
+
+    model = model.replace("replicate/", "")
+
+    messages = translate_to_string_input(messages)
+
+    output = replicate.run(
+        model,
+        input={
+            "prompt": messages,
+            "max_new_tokens": MAX_TOKENS,
+            "temperature": temperature,
+        },
+    )
+
+    # The meta/llama-2-7b model can stream output as it's running.
+    response = ""
+    # The predict method returns an iterator, and you can iterate over that output.
+    for item in output:
+        # https://replicate.com/meta/llama-2-7b/versions/527827021d8756c7ab79fde0abbfaac885c37a3ed5fe23c7465093f0878d55ef/api#output-schema
+        response += item
+
+    return response
+
+
+def hugging_face_request(model: str, messages: list[dict], temperature: float) -> str:
+    def query(payload):
+        response = requests.post(API_URL, headers=headers, json=payload)
+        return response.json()
+
+    messages = translate_to_string_input(messages)
+    API_URL = "https://xxxxxxxx.us-east-1.aws.endpoints.huggingface.cloud"
+    headers = {
+        "Authorization": "Bearer xxxxx",
+        "Content-Type": "application/json",
+    }
+
+    if temperature == 0:
+        temperature = 0.001
+
+    output = query(
+        {
+            "inputs": messages,
+            "parameters": {"temperature": temperature, "max_new_tokens": MAX_TOKENS},
+        }
+    )
+
+    return output[0]["generated_text"]
+
+
+def translate_to_string_input(
+    openai_messages: list[dict], roles_included: bool = False
+):
+    # Translate from OpenAI's dict to a single string input
+    messages = []
+    for message in openai_messages:
+        if roles_included:
+            messages.append(message["role"] + ": ")
+        messages.append(message["content"])
+    if roles_included:
+        messages.append("assistant: ")
+    return "\n".join(messages)
+
+
+# for gpt-3 models and instruct models
+def get_completions_response(
+    model: str,
+    messages: list[dict] | str,
+    temperature: float,
+    max_tokens: int = MAX_TOKENS,
+) -> str:
+    if not isinstance(messages, str):
+        prompt = translate_to_string_input(messages, roles_included=False)
+    else:
+        prompt = messages
+
+    completion = openai.Completion.create(
+        model=model, temperature=temperature, prompt=prompt, max_tokens=max_tokens
+    )
+
+    response = completion.choices[0].text
+    return response
+
+
+def count_all_tokens(model: str, messages: list[dict[str, str]]) -> int:
+    total_tokens = 0
+    for message in messages:
+        total_tokens += count_tokens(model, message["content"])
+    return total_tokens
+
+
+def count_tokens(model: str, prompt: str) -> int:
+    if "gpt" not in model:
+        model = "gpt-4"
+
+    encoding = tiktoken.encoding_for_model(model)
+    num_tokens = len(encoding.encode(prompt))
+    return num_tokens
+
+
+def get_prompt_cost(model: str, num_tokens: int) -> float:
+    # good enough for quick evals
+    if model not in pricing_dict:
+        return num_tokens * 0.001 * pricing_dict["gpt-4"]
+    return num_tokens * 0.001 * pricing_dict[model]
+
+
+def get_response_cost(model: str, num_tokens: int) -> float:
+    # good enough for quick evals
+    if model not in pricing_dict:
+        return num_tokens * 0.001 * pricing_dict["gpt-4"]
+
+    cost = num_tokens * 0.001 * pricing_dict[model]
+
+    if model == "gpt-4":
+        cost *= 2
+
+    return cost
+
+
+def record_messages(messages: list[dict], model: str):
+    # create the conversation in a human-readable format
+    conversation_text = ""
+    for message in messages:
+        conversation_text += message["content"]
+
+    # write the conversation to the next available text file
+    with open(f"gpt_outputs/transcript.txt", "w") as f:
+        f.write(model + "\n\n")
+        f.write(conversation_text)

chess-gpt-eval-contrastive/lc0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31520ad0cb827890e445ff924e974819284150a4106a7df5c6ff28b8029555b0
+size 1337758181

chess-gpt-eval-contrastive/lc0/build/release/.gitignore

@@ -0,0 +1,2 @@
+# This file is autogenerated by Meson. If you change or delete it, it won't be recreated.
+*

chess-gpt-eval-contrastive/lc0/build/release/.hgignore

@@ -0,0 +1,3 @@
+# This file is autogenerated by Meson. If you change or delete it, it won't be recreated.
+syntax: glob
+**/*

chess-gpt-eval-contrastive/lc0/build/release/.ninja_log

@@ -0,0 +1,165 @@
+# ninja log v5
+43	3010	1708374595173480000	lc0.p/src_lc0ctl_leela2onnx.cc.o	5b6753042ee0b17f
+8154	10706	1708374602869394400	liblc0_lib.so.p/src_neural_network_trivial.cc.o	5dd3e0284aaf368
+10017	10572	1708374602736488000	liblc0_lib.so.p/src_version.cc.o	666d98cf1fde3d40
+4975	7571	1708374599734290600	liblc0_lib.so.p/src_benchmark_backendbench.cc.o	cd4051b93a986d8d
+42	3246	1708374595409396600	lc0.p/src_main.cc.o	3e36effcb2e7a018
+45	2813	1708374594976621200	lc0.p/src_mcts_stoppers_stoppers.cc.o	b72d76311c272abc
+44	2791	1708374594949712500	lc0.p/src_mcts_stoppers_legacy.cc.o	a0e1457009955974
+3215	5051	1708374597214406500	lc0.p/src_utils_configfile.cc.o	d71f7bf6f56d8d26
+4942	5176	1708374597340079500	lc0.p/src_neural_shared_winograd_filter.cc.o	a47143ac9da88d48
+21	1950	1708376213982495700	liblc0_lib.so.p/src_neural_opencl_network_opencl.cc.o	e1b7352541d6eee2
+10	39	1708374592202014800	lc0.p/neural/onnx/onnx.pb.h	2d9876d98a836477
+3656	5370	1708374597534061800	lc0.p/src_utils_optionsdict.cc.o	60e5a4e4b002dc0d
+43	2370	1708374594532904400	lc0.p/src_chess_uciloop.cc.o	60ef973639dd48f4
+7819	9202	1708374601365362100	liblc0_lib.so.p/src_neural_network_legacy.cc.o	46a36e150fb1940d
+11	42	1708374592205172600	encoder_test.p/proto/net.pb.h	a8b1f637bb370875
+1617	3212	1708374595374921600	lc0.p/src_neural_loader.cc.o	2f49c4d23520037b
+43	2038	1708374594201902200	lc0.p/src_chess_board.cc.o	a21a89f8e697516f
+45	2501	1708374594659167900	lc0.p/src_neural_cache.cc.o	b069b3eafc3a29b0
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