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HN-CodeGen
Commit 26e7f36d by zhengzifu
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Update Python version to 3.11, enhance weight processing by adding layer number… 

Update Python version to 3.11, enhance weight processing by adding layer number extraction, and implement output directory cleanup for optimized and origin generation scripts.
parent c0fd4e00
Showing with 110 additions and 38 deletions
.gitignore
...@@ -2,8 +2,7 @@ __pycache__ ...@@ -2,8 +2,7 @@ __pycache__
.DS_Store .DS_Store
.venv .venv
.vscode .vscode
outputs outputs-*
outputs-qwen
weights weights
model.safetensors model.safetensors
001-H-LLM 001-H-LLM
......
hllm/config.py
...@@ -24,21 +24,30 @@ class CFG: ...@@ -24,21 +24,30 @@ class CFG:
"v.pkl", "v.pkl",
"q.pkl", "q.pkl",
] ]
self.group_numbers_batch = [
8,
32,
32,
32,
32,
32,
32,
]
self.run_weights = "down.pkl" # 用于赋值 self.run_weights = "down.pkl" # 用于赋值
self.safetensors = "model.safetensors" self.group_number = 32
self.weights_dir = "001-H-LLM/qwen" self.safetensors_path = "001-H-LLM/qwen/model.safetensors"
self.mapped_weights_dir = "001-H-LLM/qwen/mapped_weights"
self.verify_generate_activation_on_exist = False self.weights_dir = "001-H-LLM/qwen/weights"
self.mapped_weights_dir = "001-H-LLM/qwen/mapped_weights"
self.num_workers = 64 self.num_workers = 64
self.value_range = [-6, -4, -3, -2, -1.5, -1, -0.5, 0.5, 1, 1.5, 2, 3, 4, 6] self.value_range = [-6, -4, -3, -2, -1.5, -1, -0.5, 0.5, 1, 1.5, 2, 3, 4, 6]
self.python_path = sys.executable self.python_path = sys.executable
self.group_number = 32 self.output_dir = "outputs-qwen"
self.verify_generate_activation_on_exist = False
self.output_dir = "outputs"
os.makedirs(self.weights_dir, exist_ok=True) os.makedirs(self.weights_dir, exist_ok=True)
os.makedirs(self.output_dir, exist_ok=True) os.makedirs(self.output_dir, exist_ok=True)
hllm/eda/generate_quant_weights.py
import os import os
import shutil
import numpy as np import numpy as np
import safetensors import safetensors
import pickle import pickle
import re
from tqdm import tqdm from tqdm import tqdm
from hllm.eda.utils_quant import quant_and_dequant from hllm.eda.utils_quant import quant_and_dequant
from hllm.config import CFG from hllm.config import CFG
...@@ -26,10 +28,26 @@ def is_substring_in_list(substring, string_list): ...@@ -26,10 +28,26 @@ def is_substring_in_list(substring, string_list):
return any(s in substring for s in string_list) return any(s in substring for s in string_list)
def get_layer_number(key):
# 使用正则表达式匹配layers后面的数字
match = re.search(r"layers\.(\d+)\.", key)
if match:
return int(match.group(1))
return -1 # 对于不包含层号的键（如norm），返回-1
def run(config: CFG): def run(config: CFG):
file_path = os.path.join(config.weights_dir, config.safetensors) shutil.rmtree(config.weights_dir, ignore_errors=True)
os.makedirs(config.weights_dir, exist_ok=True)
print("Start generating quant weights")
file_path = config.safetensors_path
with safetensors.safe_open(file_path, framework="pt") as f: with safetensors.safe_open(file_path, framework="pt") as f:
for i, key in enumerate(tqdm(f.keys())): # 首先收集所有键并按层号排序
all_keys = [(key, get_layer_number(key)) for key in f.keys()]
sorted_keys = sorted(all_keys, key=lambda x: x[1])
for key, _ in tqdm(sorted_keys):
# print(key)
if not is_substring_in_list(key, name_dict.keys()): if not is_substring_in_list(key, name_dict.keys()):
continue continue
tensor = f.get_tensor(key) tensor = f.get_tensor(key)
...@@ -40,6 +58,19 @@ def run(config: CFG): ...@@ -40,6 +58,19 @@ def run(config: CFG):
for k in weights.keys(): for k in weights.keys():
weights[k] = np.array(weights[k]) weights[k] = np.array(weights[k])
file_path = os.path.join(config.weights_dir, f"{k}.pkl") # 保存权重文件
with open(file_path, "wb") as f: pkl_path = os.path.join(config.weights_dir, f"{k}.pkl")
with open(pkl_path, "wb") as f:
pickle.dump(weights[k], f) pickle.dump(weights[k], f)
# 保存权重信息到txt文件
info_path = os.path.join(config.weights_dir, f"{k}_info.txt")
with open(info_path, "w") as f:
f.write(f"Weight name: {k}\n")
f.write(f"Shape: {weights[k].shape}\n")
f.write(f"Data type: {weights[k].dtype}\n")
f.write(f"Max value: {np.max(weights[k])}\n")
f.write(f"Min value: {np.min(weights[k])}\n")
f.write(f"Mean value: {np.mean(weights[k])}\n")
f.write(f"Standard deviation: {np.std(weights[k])}\n")
print("Quant weights generated at", config.weights_dir)
hllm/eda/mapping_weights.py
...@@ -3,6 +3,7 @@ import os ...@@ -3,6 +3,7 @@ import os
from hllm.config import CFG from hllm.config import CFG
import pickle import pickle
from tqdm import tqdm from tqdm import tqdm
import shutil
def mapping_weights(weights, value_range): def mapping_weights(weights, value_range):
...@@ -13,9 +14,10 @@ def mapping_weights(weights, value_range): ...@@ -13,9 +14,10 @@ def mapping_weights(weights, value_range):
def run(config: CFG): def run(config: CFG):
shutil.rmtree(config.mapped_weights_dir, ignore_errors=True)
os.makedirs(config.mapped_weights_dir, exist_ok=True)
print("Start mapping weights") print("Start mapping weights")
path_dir = os.path.join(config.mapped_weights_dir) path_dir = config.mapped_weights_dir
os.makedirs(path_dir, exist_ok=True)
value_range = config.value_range value_range = config.value_range
for file in os.listdir(config.weights_dir): for file in os.listdir(config.weights_dir):
if file in config.run_weights_batch: if file in config.run_weights_batch:
......
hllm/optimized/generate_fsm.py
# %% # %%
import shutil
import sys import sys
import numpy as np import numpy as np
from pyrilog import ( from pyrilog import (
...@@ -535,6 +536,8 @@ def process_task(i, name, weights_file_name, matrix, H, L, VN, config: CFG): ...@@ -535,6 +536,8 @@ def process_task(i, name, weights_file_name, matrix, H, L, VN, config: CFG):
def run(name: str, config: CFG): def run(name: str, config: CFG):
shutil.rmtree(config.output_dir, ignore_errors=True)
os.makedirs(config.output_dir, exist_ok=True)
weights_file = os.path.join(config.weights_dir, config.run_weights) weights_file = os.path.join(config.weights_dir, config.run_weights)
weights_file_name = os.path.splitext(os.path.basename(weights_file))[0] weights_file_name = os.path.splitext(os.path.basename(weights_file))[0]
print(f"Processing {weights_file}") print(f"Processing {weights_file}")
......
hllm/optimized/generate_info.py
import os import os
import json import json
import pickle import pickle
import shutil
import numpy as np import numpy as np
from tqdm import tqdm from tqdm import tqdm
from multiprocessing import Pool from multiprocessing import Pool
...@@ -34,6 +35,8 @@ def process_weight(args): ...@@ -34,6 +35,8 @@ def process_weight(args):
def run(name: str, config: CFG): def run(name: str, config: CFG):
shutil.rmtree(config.output_dir, ignore_errors=True)
os.makedirs(config.output_dir, exist_ok=True)
weights_file = os.path.join(config.mapped_weights_dir, config.run_weights) weights_file = os.path.join(config.mapped_weights_dir, config.run_weights)
weights_file_name = os.path.splitext(os.path.basename(weights_file))[0] weights_file_name = os.path.splitext(os.path.basename(weights_file))[0]
print(f"Processing {weights_file_name}") print(f"Processing {weights_file_name}")
......
hllm/optimized/generate_layer_mux.py
...@@ -21,6 +21,7 @@ import os ...@@ -21,6 +21,7 @@ import os
from hllm.config import CFG from hllm.config import CFG
from tqdm import tqdm from tqdm import tqdm
import pickle import pickle
import shutil
from hllm.utils import calculate_WW from hllm.utils import calculate_WW
...@@ -106,6 +107,8 @@ def process_task(i, name, weights_file_name, matrix, H, L, config: CFG): ...@@ -106,6 +107,8 @@ def process_task(i, name, weights_file_name, matrix, H, L, config: CFG):
def run(name: str, config: CFG): def run(name: str, config: CFG):
shutil.rmtree(config.output_dir, ignore_errors=True)
os.makedirs(config.output_dir, exist_ok=True)
weights_file = os.path.join(config.weights_dir, config.run_weights) weights_file = os.path.join(config.weights_dir, config.run_weights)
weights_file_name = os.path.splitext(os.path.basename(weights_file))[0] weights_file_name = os.path.splitext(os.path.basename(weights_file))[0]
print(f"Processing {weights_file}") print(f"Processing {weights_file}")
......
hllm/optimized/generate_mid_wrapper.py
# %% # %%
import json import json
import sys import sys
import shutil
import numpy as np import numpy as np
from pyrilog import ( from pyrilog import (
VerilogGenerator, VerilogGenerator,
...@@ -68,18 +69,18 @@ def generate_module( ...@@ -68,18 +69,18 @@ def generate_module(
sw_ports[f"WT_{j}_out_S"] = f"WT_{j}_out_S[{i}]" sw_ports[f"WT_{j}_out_S"] = f"WT_{j}_out_S[{i}]"
sw_ports[f"WT_{j}_out_C"] = f"WT_{j}_out_C[{i}]" sw_ports[f"WT_{j}_out_C"] = f"WT_{j}_out_C[{i}]"
add_instance( add_instance(
f"Sub_wrapper_tp_{weights_file_name}_vc_{cur_GP*GN+i}", f"Sub_wrapper_tp_{weights_file_name}_vc_{cur_GP * GN + i}",
f"Sub_wrapper_{cur_GP*GN+i}", f"Sub_wrapper_{cur_GP * GN + i}",
None, None,
sw_ports, sw_ports,
) )
tcl.add_dependency( tcl.add_dependency(
f"Sub_wrapper", f"Sub_wrapper",
f"Sub_wrapper_tp_{weights_file_name}_vc_{cur_GP*GN+i}.sv", f"Sub_wrapper_tp_{weights_file_name}_vc_{cur_GP * GN + i}.sv",
) )
tcl.add_dependency( tcl.add_dependency(
f"WT_group", f"WT_group",
f"WT_group_tp_{weights_file_name}_vc_{cur_GP*GN+i}.sv", f"WT_group_tp_{weights_file_name}_vc_{cur_GP * GN + i}.sv",
) )
tcl.add_dependency( tcl.add_dependency(
f"Mid_wrapper", f"Mid_wrapper",
...@@ -87,7 +88,7 @@ def generate_module( ...@@ -87,7 +88,7 @@ def generate_module(
) )
tcl.add_dependency( tcl.add_dependency(
f"Layer_mux", f"Layer_mux",
f"Layer_mux_tp_{weights_file_name}_vc_{cur_GP*GN+i}.sv", f"Layer_mux_tp_{weights_file_name}_vc_{cur_GP * GN + i}.sv",
) )
tcl.add_dependency( tcl.add_dependency(
f"FSM", f"FSM",
...@@ -96,11 +97,11 @@ def generate_module( ...@@ -96,11 +97,11 @@ def generate_module(
for j in range(len(value_range)): for j in range(len(value_range)):
tcl.add_dependency( tcl.add_dependency(
f"Mux_wrapper", f"Mux_wrapper",
f"Mux_wrapper_tp_{weights_file_name}_vc_{cur_GP*GN+i}_value_{j}.sv", f"Mux_wrapper_tp_{weights_file_name}_vc_{cur_GP * GN + i}_value_{j}.sv",
) )
tcl.add_dependency( tcl.add_dependency(
f"Mux", f"Mux",
f"Mux_tp_{weights_file_name}_vc_{cur_GP*GN+i}_value_{j}.sv", f"Mux_tp_{weights_file_name}_vc_{cur_GP * GN + i}_value_{j}.sv",
) )
for line in ww_list[cur_GP * GN + i]: for line in ww_list[cur_GP * GN + i]:
tcl.add_dependency( tcl.add_dependency(
...@@ -146,6 +147,8 @@ def process_task(i, name, weights_file_name, ww_list, H, L, VN, config: CFG): ...@@ -146,6 +147,8 @@ def process_task(i, name, weights_file_name, ww_list, H, L, VN, config: CFG):
def run(name: str, config: CFG): def run(name: str, config: CFG):
shutil.rmtree(config.output_dir, ignore_errors=True)
os.makedirs(config.output_dir, exist_ok=True)
weights_file = os.path.join(config.weights_dir, config.run_weights) weights_file = os.path.join(config.weights_dir, config.run_weights)
weights_file_name = os.path.splitext(os.path.basename(weights_file))[0] weights_file_name = os.path.splitext(os.path.basename(weights_file))[0]
print(f"Processing {weights_file}") print(f"Processing {weights_file}")
......
hllm/optimized/generate_mux.py
...@@ -22,6 +22,7 @@ from hllm.config import CFG ...@@ -22,6 +22,7 @@ from hllm.config import CFG
from tqdm import tqdm from tqdm import tqdm
import pickle import pickle
import json import json
import shutil
# %% # %%
...@@ -119,6 +120,8 @@ def process_task(i, name, weights_file_name, matrix, H, L, config: CFG): ...@@ -119,6 +120,8 @@ def process_task(i, name, weights_file_name, matrix, H, L, config: CFG):
def run(name: str, config: CFG): def run(name: str, config: CFG):
shutil.rmtree(config.output_dir, ignore_errors=True)
os.makedirs(config.output_dir, exist_ok=True)
weights_file = os.path.join(config.mapped_weights_dir, config.run_weights) weights_file = os.path.join(config.mapped_weights_dir, config.run_weights)
weights_file_name = os.path.splitext(os.path.basename(weights_file))[0] weights_file_name = os.path.splitext(os.path.basename(weights_file))[0]
print(f"Processing {weights_file_name}") print(f"Processing {weights_file_name}")
......
hllm/optimized/generate_mux_wrapper.py
...@@ -23,6 +23,7 @@ from hllm.config import CFG ...@@ -23,6 +23,7 @@ from hllm.config import CFG
from tqdm import tqdm from tqdm import tqdm
import pickle import pickle
import json import json
import shutil
# %% # %%
...@@ -105,6 +106,7 @@ def process_task(i, name, weights_file_name, H, L, config: CFG): ...@@ -105,6 +106,7 @@ def process_task(i, name, weights_file_name, H, L, config: CFG):
def run(name: str, config: CFG): def run(name: str, config: CFG):
shutil.rmtree(config.output_dir, ignore_errors=True)
os.makedirs(config.output_dir, exist_ok=True) os.makedirs(config.output_dir, exist_ok=True)
weights_file = os.path.join(config.mapped_weights_dir, config.run_weights) weights_file = os.path.join(config.mapped_weights_dir, config.run_weights)
weights_file_name = os.path.splitext(os.path.basename(weights_file))[0] weights_file_name = os.path.splitext(os.path.basename(weights_file))[0]
......
hllm/origin/generate_fsm.py
# %% # %%
import shutil
import sys import sys
import numpy as np import numpy as np
from pyrilog import ( from pyrilog import (
...@@ -537,6 +538,11 @@ def process_task(i, name, weights_file_name, matrix, H, L, VN, config: CFG): ...@@ -537,6 +538,11 @@ def process_task(i, name, weights_file_name, matrix, H, L, VN, config: CFG):
def run(name: str, config: CFG): def run(name: str, config: CFG):
weights_file = os.path.join(config.weights_dir, config.run_weights) weights_file = os.path.join(config.weights_dir, config.run_weights)
weights_file_name = os.path.splitext(os.path.basename(weights_file))[0] weights_file_name = os.path.splitext(os.path.basename(weights_file))[0]
output_dir = os.path.join(config.output_dir, name, weights_file_name)
shutil.rmtree(output_dir, ignore_errors=True)
os.makedirs(output_dir, exist_ok=True)
print(f"Processing {weights_file}") print(f"Processing {weights_file}")
with open(weights_file, "rb") as f: with open(weights_file, "rb") as f:
matrixs = pickle.load(f) matrixs = pickle.load(f)
...@@ -555,5 +561,4 @@ def run(name: str, config: CFG): ...@@ -555,5 +561,4 @@ def run(name: str, config: CFG):
result = future.result() result = future.result()
except Exception as e: except Exception as e:
print(f"Generating {result} failed with an error: {e}") print(f"Generating {result} failed with an error: {e}")
file_dir = os.path.join(config.output_dir, name, weights_file_name) print("Files generated in", output_dir)
print("Files generated in", file_dir)
hllm/origin/generate_hn.py
# %% # %%
import sys import sys
import shutil
import numpy as np import numpy as np
from pyrilog import ModuleBlock, add_parameter, add_input, add_output, add_assign from pyrilog import ModuleBlock, add_parameter, add_input, add_output, add_assign
from concurrent.futures import ProcessPoolExecutor, as_completed from concurrent.futures import ProcessPoolExecutor, as_completed
...@@ -85,6 +86,8 @@ def process_task(i, name, weights_file_name, matrix, H, L, config: CFG): ...@@ -85,6 +86,8 @@ def process_task(i, name, weights_file_name, matrix, H, L, config: CFG):
def run(name: str, config: CFG): def run(name: str, config: CFG):
shutil.rmtree(config.output_dir, ignore_errors=True)
os.makedirs(config.output_dir, exist_ok=True)
weights_file = os.path.join(config.weights_dir, config.run_weights) weights_file = os.path.join(config.weights_dir, config.run_weights)
weights_file_name = os.path.splitext(os.path.basename(weights_file))[0] weights_file_name = os.path.splitext(os.path.basename(weights_file))[0]
print(f"Processing {weights_file}") print(f"Processing {weights_file}")
......
hllm/origin/generate_layer_mux.py
# %% # %%
import sys import sys
import shutil
import numpy as np import numpy as np
from pyrilog import ( from pyrilog import (
VerilogGenerator, VerilogGenerator,
...@@ -105,6 +106,8 @@ def process_task(i, name, weights_file_name, matrix, H, L, config: CFG): ...@@ -105,6 +106,8 @@ def process_task(i, name, weights_file_name, matrix, H, L, config: CFG):
def run(name: str, config: CFG): def run(name: str, config: CFG):
shutil.rmtree(config.output_dir, ignore_errors=True)
os.makedirs(config.output_dir, exist_ok=True)
weights_file = os.path.join(config.weights_dir, config.run_weights) weights_file = os.path.join(config.weights_dir, config.run_weights)
weights_file_name = os.path.splitext(os.path.basename(weights_file))[0] weights_file_name = os.path.splitext(os.path.basename(weights_file))[0]
print(f"Processing {weights_file}") print(f"Processing {weights_file}")
......
hllm/origin/generate_mid_wrapper.py
# %% # %%
import sys import sys
import shutil
import numpy as np import numpy as np
from pyrilog import ( from pyrilog import (
VerilogGenerator, VerilogGenerator,
...@@ -66,26 +67,26 @@ def generate_module( ...@@ -66,26 +67,26 @@ def generate_module(
sw_ports[f"WT_{j}_out_S"] = f"WT_{j}_out_S[{i}]" sw_ports[f"WT_{j}_out_S"] = f"WT_{j}_out_S[{i}]"
sw_ports[f"WT_{j}_out_C"] = f"WT_{j}_out_C[{i}]" sw_ports[f"WT_{j}_out_C"] = f"WT_{j}_out_C[{i}]"
add_instance( add_instance(
f"Sub_wrapper_tp_{weights_file_name}_vc_{cur_GP*GN+i}", f"Sub_wrapper_tp_{weights_file_name}_vc_{cur_GP * GN + i}",
f"Sub_wrapper_{cur_GP*GN+i}", f"Sub_wrapper_{cur_GP * GN + i}",
None, None,
sw_ports, sw_ports,
) )
tcl.add_dependency( tcl.add_dependency(
f"HN", f"HN",
f"HN_tp_{weights_file_name}_vc_{cur_GP*GN+i}.sv", f"HN_tp_{weights_file_name}_vc_{cur_GP * GN + i}.sv",
) )
tcl.add_dependency( tcl.add_dependency(
f"WT_group", f"WT_group",
f"WT_group_tp_{weights_file_name}_vc_{cur_GP*GN+i}.sv", f"WT_group_tp_{weights_file_name}_vc_{cur_GP * GN + i}.sv",
) )
tcl.add_dependency( tcl.add_dependency(
f"Layer_mux", f"Layer_mux",
f"Layer_mux_tp_{weights_file_name}_vc_{cur_GP*GN+i}.sv", f"Layer_mux_tp_{weights_file_name}_vc_{cur_GP * GN + i}.sv",
) )
tcl.add_dependency( tcl.add_dependency(
f"Sub_wrapper", f"Sub_wrapper",
f"Sub_wrapper_tp_{weights_file_name}_vc_{cur_GP*GN+i}.sv", f"Sub_wrapper_tp_{weights_file_name}_vc_{cur_GP * GN + i}.sv",
) )
tcl.add_dependency( tcl.add_dependency(
f"Mid_wrapper", f"Mid_wrapper",
...@@ -138,6 +139,8 @@ def process_task(i, name, weights_file_name, ww_list, H, L, VN, config: CFG): ...@@ -138,6 +139,8 @@ def process_task(i, name, weights_file_name, ww_list, H, L, VN, config: CFG):
def run(name: str, config: CFG): def run(name: str, config: CFG):
shutil.rmtree(config.output_dir, ignore_errors=True)
os.makedirs(config.output_dir, exist_ok=True)
weights_file = os.path.join(config.weights_dir, config.run_weights) weights_file = os.path.join(config.weights_dir, config.run_weights)
weights_file_name = os.path.splitext(os.path.basename(weights_file))[0] weights_file_name = os.path.splitext(os.path.basename(weights_file))[0]
print(f"Processing {weights_file}") print(f"Processing {weights_file}")
......
main.py
...@@ -52,10 +52,9 @@ def run_optimized(config: CFG): ...@@ -52,10 +52,9 @@ def run_optimized(config: CFG):
def run_weights_preprocess(config: CFG): def run_weights_preprocess(config: CFG):
import hllm.eda.generate_quant_weights as generate_quant_weights import hllm.eda.generate_quant_weights as generate_quant_weights
import hllm.eda.mapping_weights as generate_mapping_weights import hllm.eda.mapping_weights as generate_mapping_weights
generate_quant_weights.run(config=config) generate_quant_weights.run(config=config)
generate_mapping_weights.run(config=config) generate_mapping_weights.run(config=config)
def run_verify(config: CFG): def run_verify(config: CFG):
...@@ -68,8 +67,9 @@ def run_verify(config: CFG): ...@@ -68,8 +67,9 @@ def run_verify(config: CFG):
def batch_run(config: CFG): def batch_run(config: CFG):
for weights in config.run_weights_batch: for i in range(len(config.run_weights_batch)):
config.run_weights = weights config.run_weights = config.run_weights_batch[i]
config.group_number = config.group_numbers_batch[i]
run_origin(config) run_origin(config)
run_optimized(config) run_optimized(config)
run_verify(config) run_verify(config)
...@@ -77,8 +77,8 @@ def batch_run(config: CFG): ...@@ -77,8 +77,8 @@ def batch_run(config: CFG):
if __name__ == "__main__": if __name__ == "__main__":
config = CFG() config = CFG()
# run_weights_preprocess(config) run_weights_preprocess(config)
# run_origin(config) # run_origin(config)
# run_optimized(config) # run_optimized(config)
# run_verify() # run_verify()
batch_run(config) # batch_run(config)
\ No newline at end of file
todo.md 0 → 100644
- 增加生成前删除原有的文件，目前只改了 optimized 的 fsm
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