diff --git a/.github/workflows/quality.yml b/.github/workflows/quality.yml index 3e2012d..278ede5 100644 --- a/.github/workflows/quality.yml +++ b/.github/workflows/quality.yml @@ -56,7 +56,7 @@ jobs: run: uv python install 3.12 - name: Install locked dependencies - run: uv sync --locked --extra legacy-ui + run: uv sync --locked - name: Lint supported architecture boundary run: >- diff --git a/pages/Blueprinting/overview.py b/pages/Blueprinting/overview.py deleted file mode 100644 index acfee0b..0000000 --- a/pages/Blueprinting/overview.py +++ /dev/null @@ -1,40 +0,0 @@ -"""Blueprinting single-point analysis workbench.""" - -import streamlit as st - -from blueprinting.workbench.streamlit_ui import ( - analysis_form, - cached_analysis, - recalled, - remember, - render_analysis_summary, - setup_workbench_page, -) - -setup_workbench_page("分析总览", "🧭") -st.caption( - "从模型语义和并行策略推导 PortablePlanIR，再以版本化硬件证据估算延迟与内存。" - "Calculon 不参与这条产品分析路径。" -) - -STATE_KEY = "blueprinting.analysis.last_result" -draft = analysis_form("blueprinting.overview") -if draft is not None: - with st.spinner("正在进行形式化推导与证据估算…"): - remember(STATE_KEY, cached_analysis(draft)) - -outcome = recalled(STATE_KEY) -if outcome is None: - st.info("从左侧选择模型、硬件证据和执行策略，然后运行 Blueprinting 分析。") - st.markdown( - """ - 这条工作台路径提供： - - - 类型化配置校验与可复现 request digest； - - ModelIR → DistributedTaskIR → PortablePlanIR 推导； - - 计算、访存、通信、Pipeline bubble 和单设备内存估算； - - 明确的证据版本、实现边界与失败诊断。 - """ - ) -else: - render_analysis_summary(outcome) diff --git a/pages/LLM_Calc/blockwise.py b/pages/LLM_Calc/blockwise.py deleted file mode 100755 index 8297f96..0000000 --- a/pages/LLM_Calc/blockwise.py +++ /dev/null @@ -1,48 +0,0 @@ -"""LLM 训练计算器 - 块粒度视图页面""" - -import streamlit as st -import hyperparameter as hp - -from blueprinting.ui import ( - setup_page, - setup_sidebar, - page_header_with_config, - page_title, - section_header, -) -from blueprinting.st import attention_block, block, ffn_block - - -# ============================================================================ -# 页面初始化 -# ============================================================================ -setup_page(title="LLM训练计算器 - 块粒度") -app_json, sys_json, exe_json = setup_sidebar() - -page_title( - "块粒度分析", - subtitle="Transformer 块级结构分析和 IR 编译模拟", - icon="🧱" -) - - -# ============================================================================ -# 主要超参配置 -# ============================================================================ -with hp.scope(app=app_json, model=app_json, sys=sys_json, exe=exe_json) as ps: - config = page_header_with_config("主要超参", ps, mbs_param="exe.micro_batch_size") - use_humanreadable = config["use_humanreadable"] - use_raw_output = config["use_raw_output"] - - # ======================================================================== - # 块粒度视图 - # ======================================================================== - section_header("Transformer 块结构", "可视化 Transformer 块的内部组件") - - with st.expander("⚙️ 当前配置", expanded=False): - st.json(ps.storage().storage()) - - with block("transformer_block", 6): - st.markdown("### 🔷 Transformer Block") - ffn_block() - attention_block() diff --git a/pages/LLM_Calc/distexp.py b/pages/LLM_Calc/distexp.py deleted file mode 100755 index 2a8c164..0000000 --- a/pages/LLM_Calc/distexp.py +++ /dev/null @@ -1,238 +0,0 @@ -"""LLM 训练计算器 - 分布式实验页面""" - -import logging - -import pandas as pd -import plotly.express as px -import streamlit as st -import hyperparameter as hp - -from calculon.llm import Llm -from calculon.system import System -from blueprinting.types import Execution, Model -from blueprinting.ui import ( - setup_page, - setup_sidebar, - page_header_with_config, - page_title, - section_header, - info_card, -) - - -# ============================================================================ -# 页面初始化 -# ============================================================================ -setup_page(title="LLM训练计算器 - 分布式实验") -app_json, sys_json, exe_json = setup_sidebar() -logger = logging.getLogger() - -page_title( - "分布式实验", - subtitle="探索不同 TP/PP/DP 并行配置对训练性能的影响", - icon="🔄" -) - - -# ============================================================================ -# 辅助函数 -# ============================================================================ -def gen_nums(rng): - """生成范围内的 2 的幂次数列""" - left, right = rng - for i in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]: - if left <= i <= right: - yield i - - -def run_experiment(ps, app_json, sys_json, tp, pp, dp, logger): - """运行单次实验，返回 TGS 或 None""" - with hp.scope() as ps_exp: - ps_exp.exe.tensor_par = tp - ps_exp.exe.pipeline_par = pp - ps_exp.exe.data_par = dp - ps_exp.exe.num_procs = tp * pp * dp - - app = Model.from_cfg(ps.app) - exe = Execution(ps_exp.exe) - syst = System(sys_json) - - try: - model = Llm(app, logger) - model.compile(syst, exe) - model.run(syst) - - stats = model.get_stats_json(False) - tgs = ( - app.seq_size - * exe.global_batch_size - / stats["total_time"] - / (exe.tensor_par * exe.pipeline_par * exe.data_par) - ) - return tgs - except Exception: - return None - - -def plot_analysis(results, x_col, y_col, color_col, filter_col, filter_values, title_prefix): - """绘制分析图表""" - c1, c2 = st.columns(2) - for i, val in enumerate(filter_values): - filtered = results[results[filter_col] == val] - col = c1 if i % 2 == 0 else c2 - with col: - st.plotly_chart( - px.line( - filtered, - x=x_col, - y=y_col, - color=color_col, - title=f"{title_prefix}[{filter_col}={val}]", - ), - use_container_width=True, - ) - - -# ============================================================================ -# 主要超参配置 -# ============================================================================ -with hp.scope(app=app_json, model=app_json, sys=sys_json, exe=exe_json) as ps: - config = page_header_with_config("主要超参", ps, use_slider=True) - use_humanreadable = config["use_humanreadable"] - use_raw_output = config["use_raw_output"] - - # ======================================================================== - # 运行实验 - # ======================================================================== - def gen_setups(): - """生成所有实验配置组合""" - for tp in gen_nums(ps.exp.tp | [1, 1]): - for pp in gen_nums(ps.exp.pp | [1, 1]): - for dp in gen_nums(ps.exp.dp | [1, 1]): - yield tp, pp, dp - - results = [] - for tp, pp, dp in gen_setups(): - tgs = run_experiment(ps, app_json, sys_json, tp, pp, dp, logger) - results.append({ - "tp": tp, - "pp": pp, - "dp": dp, - "tgs": tgs, - }) - - results = pd.DataFrame.from_records(results) - - # ======================================================================== - # 实验数据展示 - # ======================================================================== - with st.expander("实验数据", expanded=True): - st.dataframe(results, use_container_width=True) - - # ======================================================================== - # TP 分析 - # ======================================================================== - tp_range = ps.exp.tp | [1, 1] - if tp_range[1] > tp_range[0]: - with st.expander("TP分析", expanded=True): - c1, c2 = st.columns(2) - - # 按 PP 分组 - with c1: - for pp in gen_nums(ps.exp.pp | [1, 1]): - st.plotly_chart( - px.line( - results[results.pp == pp], - x="tp", - y="tgs", - color="dp", - title=f"TP分析[pp={pp}]", - ), - use_container_width=True, - ) - - # 按 DP 分组 - with c2: - for dp in gen_nums(ps.exp.dp | [1, 1]): - st.plotly_chart( - px.line( - results[results.dp == dp], - x="tp", - y="tgs", - color="pp", - title=f"TP分析[dp={dp}]", - ), - use_container_width=True, - ) - - # ======================================================================== - # PP 分析 - # ======================================================================== - pp_range = ps.exp.pp | [1, 1] - if pp_range[1] > pp_range[0]: - with st.expander("PP分析", expanded=True): - c1, c2 = st.columns(2) - - # 按 TP 分组 - with c1: - for tp in gen_nums(ps.exp.tp | [1, 1]): - st.plotly_chart( - px.line( - results[results.tp == tp], - x="pp", - y="tgs", - color="dp", - title=f"PP分析[tp={tp}]", - ), - use_container_width=True, - ) - - # 按 DP 分组 - with c2: - for dp in gen_nums(ps.exp.dp | [1, 1]): - st.plotly_chart( - px.line( - results[results.dp == dp], - x="pp", - y="tgs", - color="tp", - title=f"PP分析[dp={dp}]", - ), - use_container_width=True, - ) - - # ======================================================================== - # DP 分析 - # ======================================================================== - dp_range = ps.exp.dp | [1, 1] - if dp_range[1] > dp_range[0]: - with st.expander("DP分析", expanded=True): - c1, c2 = st.columns(2) - - # 按 TP 分组 - with c1: - for tp in gen_nums(ps.exp.tp | [1, 1]): - st.plotly_chart( - px.line( - results[results.tp == tp], - x="dp", - y="tgs", - color="pp", - title=f"DP分析[tp={tp}]", - ), - use_container_width=True, - ) - - # 按 PP 分组 - with c2: - for pp in gen_nums(ps.exp.pp | [1, 1]): - st.plotly_chart( - px.line( - results[results.pp == pp], - x="dp", - y="tgs", - color="tp", - title=f"DP分析[pp={pp}]", - ), - use_container_width=True, - ) diff --git a/pages/LLM_Calc/overview.py b/pages/LLM_Calc/overview.py deleted file mode 100755 index f622187..0000000 --- a/pages/LLM_Calc/overview.py +++ /dev/null @@ -1,263 +0,0 @@ -"""LLM 训练计算器 - 总览页面""" - -import json -import logging -from contextlib import nullcontext - -import pandas as pd -import plotly.express as px -import streamlit as st -import hyperparameter as hp -from streamlit_extras.add_vertical_space import add_vertical_space -from streamlit_extras.row import row - -from calculon.llm import Llm -from calculon.system import System -from blueprinting.types import Execution, Model -from blueprinting.ui import ( - human_readable_flops, - human_readable_num, - make_summary, - setup_page, - setup_sidebar, - page_header_with_config, - transformer_config_expander, - raw_output_section, - page_title, - metrics_row, - section_header, - info_card, -) - - -# ============================================================================ -# 页面初始化 -# ============================================================================ -setup_page(title="LLM训练计算器") -app_json, sys_json, exe_json = setup_sidebar() -logger = logging.getLogger() - -# 页面标题 -page_title( - "LLM 训练计算器", - subtitle="分析大语言模型训练的计算、通信和内存开销", - icon="🧮" -) - - -# ============================================================================ -# 主要超参配置 -# ============================================================================ -with hp.scope(app=app_json, model=app_json, sys=sys_json, exe=exe_json) as ps: - config = page_header_with_config("主要超参", ps) - use_humanreadable = config["use_humanreadable"] - use_raw_output = config["use_raw_output"] - - # 编译模型 - app = Model.from_cfg(ps.app) - exe = Execution(ps.exe) - syst = System(sys_json) - - model = Llm(app, logger) - model.compile(syst, exe) - model.run(syst) - - -# ============================================================================ -# 页面内容 -# ============================================================================ -tab_model, tab_detail = st.tabs(["📊 模型 Overview", "⚙️ 执行细节"]) - - -# ============================================================================ -# 模型 Overview Tab -# ============================================================================ -with tab_model, ps: - # Transformer 参数配置 - model_config = transformer_config_expander(ps) - - with hp.scope(**model_config) as ps: - stats = model.get_stats_json(False) - - # 原始输出（可选） - if use_raw_output: - raw_output_section(stats, make_summary) - - # 整体性能指标 - 使用醒目的布局 - section_header("整体性能", "训练迭代时间和吞吐量") - - tgs = ( - app.seq_size - * exe.global_batch_size - / stats["total_time"] - / (exe.tensor_par * exe.pipeline_par * exe.data_par) - ) - - metrics_row( - ("⏱️ 迭代时间", "%.2f s" % stats["total_time"], None, "单次训练迭代所需时间"), - ("🚀 TGS", "%.2f" % tgs, None, "每秒处理的 Token 数 (per GPU)"), - ("🔢 总参数量", human_readable_num(app.nparam_total), None, "模型总参数量"), - ("💻 计算量", human_readable_flops(app.flops_total * 3), None, "单次迭代总 FLOPs"), - ) - - st.markdown("") # 间距 - - # 参数量和计算量分析 - col1, col2 = st.columns(2) - - with col1: - with st.expander("📦 参数量分析", expanded=True): - st.dataframe( - pd.DataFrame.from_records([ - { - "层级": "🔹 单块", - "Embedding": "-", - "Attention": app.nparam_attn, - "归一化": app.nparam_norm, - "FFN": app.nparam_mlp, - "合计": app.nparam_attn + app.nparam_norm + app.nparam_mlp, - }, - { - "层级": "🔸 整体", - "Embedding": app.nparam_embedding, - "Attention": app.nparam_attn * app.num_blocks, - "归一化": app.nparam_norm * app.num_blocks, - "FFN": app.nparam_mlp * app.num_blocks, - "合计": app.nparam_total, - }, - ]).style.format( - {col: human_readable_num for col in ["Embedding", "Attention", "归一化", "FFN", "合计"]} - if use_humanreadable else None - ), - use_container_width=True, - hide_index=True, - ) - - with col2: - with st.expander("⚡ 计算量分析 (FLOPs)", expanded=True): - st.dataframe( - pd.DataFrame.from_records([ - { - "层级": "🔹 单块", - "Embedding": "-", - "Attention": app.flops_attn, - "归一化": app.flops_norm, - "FFN": app.flops_mlp, - "合计": app.flops_attn + app.flops_norm + app.flops_mlp, - }, - { - "层级": "🔸 整体×3", - "Embedding": 3 * app.flops_embedding, - "Attention": 3 * app.flops_attn * app.num_blocks, - "归一化": 3 * app.flops_norm * app.num_blocks, - "FFN": 3 * app.flops_mlp * app.num_blocks, - "合计": 3 * app.flops_total, - }, - ]).style.format( - {col: human_readable_flops for col in ["Embedding", "Attention", "归一化", "FFN", "合计"]} - if use_humanreadable else None - ), - use_container_width=True, - hide_index=True, - ) - - # 详细计算量表格（折叠） - with st.expander("📋 详细计算量分解", expanded=False): - st.dataframe( - pd.DataFrame.from_records([ - {"阶段": "块前向", "Embedding": 0.0, "Attention": app.flops_attn, "归一化": app.flops_norm, "FFN": app.flops_mlp}, - {"阶段": "块反向", "Embedding": 0.0, "Attention": 2 * app.flops_attn, "归一化": 2 * app.flops_norm, "FFN": 2 * app.flops_mlp}, - {"阶段": "整体前向", "Embedding": app.flops_embedding, "Attention": app.flops_attn * app.num_blocks, "归一化": app.flops_norm * app.num_blocks, "FFN": app.flops_norm * app.num_blocks, "总计": app.flops_total}, - {"阶段": "整体反向", "Embedding": 2 * app.flops_embedding, "Attention": 2 * app.flops_attn * app.num_blocks, "归一化": 2 * app.flops_norm * app.num_blocks, "FFN": 2 * app.flops_norm * app.num_blocks, "总计": 2 * app.flops_total}, - ]).style.format(human_readable_flops if use_humanreadable else None), - use_container_width=True, - hide_index=True, - ) - - # 通信统计 - with st.expander("📡 通信量分析", expanded=False): - st.caption("通信操作: all reduce / all gather / reduce scatter / send recv") - comm_data = pd.DataFrame.from_records([ - {"阶段": "块前向", "Embedding": app.comm_embedding_fw, "Attention": app.comm_attn_fw, "FFN": app.comm_mlp_fw}, - {"阶段": "块反向", "Embedding": app.comm_embedding_bw, "Attention": app.comm_attn_bw, "FFN": app.comm_mlp_bw}, - {"阶段": "整体前向", "Embedding": app.comm_embedding_fw, "Attention": app.comm_attn_fw * app.num_blocks, "FFN": app.comm_mlp_fw * app.num_blocks, "总计": app.comm_total_fw * app.num_blocks + app.comm_pipeline_parallel_fw}, - {"阶段": "整体反向", "Embedding": app.comm_embedding_bw, "Attention": app.comm_attn_bw * app.num_blocks, "FFN": app.comm_mlp_bw * app.num_blocks, "总计": app.comm_total_bw * app.num_blocks + app.comm_pipeline_parallel_bw}, - ]) - st.dataframe( - comm_data.map(lambda x: str(x).replace("\n", " | ") if isinstance(x, str) else x), - use_container_width=True, - hide_index=True, - ) - - -# ============================================================================ -# 执行细节 Tab -# ============================================================================ -with tab_detail: - if use_raw_output: - raw_output_section(stats, make_summary) - - stats_scope = hp.scope(**stats) - - section_header("阶段分解", "各训练阶段的 FLOPs 和显存访问量") - - def make_detail(da, title, expanded=True): - """渲染详细信息表格和图表""" - with st.expander(title, expanded=expanded): - c1, c2 = st.columns([0.35, 0.65]) - with c1: - st.dataframe(da, use_container_width=True, hide_index=True) - with c2: - fig = px.bar( - da, - x="stage", - y="value", - color="stage", - color_discrete_sequence=px.colors.qualitative.Set2, - ) - fig.update_layout( - showlegend=False, - margin=dict(l=20, r=20, t=30, b=20), - xaxis_title="", - yaxis_title="", - ) - st.plotly_chart(fig, use_container_width=True) - - col1, col2 = st.columns(2) - - # FLOPs 详情 - with col1: - make_detail( - pd.DataFrame({ - "stage": ["前向", "激活梯度", "权重梯度", "优化器"], - "value": [ - stats_scope.block_fw_flops | 0, - stats_scope.block_agrad_flops | 0, - stats_scope.block_wgrad_flops | 0, - stats_scope.block_optim_flops | 0, - ], - }), - "⚡ FLOPs 分布", - ) - - # 显存详情 - with col2: - make_detail( - pd.DataFrame({ - "stage": ["前向", "激活梯度", "权重梯度", "优化器"], - "value": [ - stats_scope.block_fw_mem_accessed | 0, - stats_scope.block_agrad_mem_accessed | 0, - stats_scope.block_wgrad_mem_accessed | 0, - stats_scope.block_optim_mem_accessed | 0, - ], - }), - "💾 显存访问量", - ) - - # 提示信息 - info_card( - "分析说明", - "FLOPs 分布展示了各训练阶段的计算量，显存访问量反映了数据移动开销。优化器阶段通常占用较少的 FLOPs 但可能有较多的显存访问。", - icon="💡" - ) diff --git a/pyproject.toml b/pyproject.toml index 48a38e3..c598641 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,11 +41,9 @@ classifiers = [ ] dependencies = [ - "hyperparameter>=0.4.0", "numpy>=1.20.0", "pandas>=1.3.0", "rich>=12.0.0", - "sympy>=1.12.1", "nicegui>=3.15,<4", "psutil>=5.9.0", ] @@ -54,12 +52,7 @@ dependencies = [ performance-data = [ "pyarrow>=12.0.0", ] -legacy-ui = [ - "streamlit>=1.53,<2", - "streamlit-extras>=0.6,<1", -] full = [ - "blueprinting[legacy-ui]", "plotly>=5.0.0", ] dev = [ @@ -111,10 +104,9 @@ include = [ ] exclude = [ "/.github", - "/docs", - "/examples", - "/pages", - "/data/evidence", + "/docs", + "/examples", + "/data/evidence", ] [tool.hatch.build.targets.wheel] @@ -262,6 +254,4 @@ dev = [ "pytest-asyncio>=0.24.0", "pytest-cov>=4.0.0", "ruff>=0.1.0", - "streamlit>=1.53,<2", - "streamlit-extras>=0.6,<1", ] diff --git a/src/blueprinting/console.py b/src/blueprinting/console.py deleted file mode 100755 index 58bbb29..0000000 --- a/src/blueprinting/console.py +++ /dev/null @@ -1,48 +0,0 @@ -from typing import Optional - -import pandas as pd -from rich import box -from rich.console import Console -from rich.table import Table - -console = Console() - - -def df_to_table( - pandas_dataframe: pd.DataFrame, - rich_table: Table, - show_index: bool = False, - index_name: Optional[str] = None, -) -> Table: - """Convert a pandas.DataFrame obj into a rich.Table obj. - Args: - pandas_dataframe (DataFrame): A Pandas DataFrame to be converted to a rich Table. - rich_table (Table): A rich Table that should be populated by the DataFrame values. - show_index (bool): Add a column with a row count to the table. Defaults to True. - index_name (str, optional): The column name to give to the index column. Defaults to None, showing no value. - Returns: - Table: The rich Table instance passed, populated with the DataFrame values.""" - - if show_index: - index_name = str(index_name) if index_name else "" - rich_table.add_column(index_name) - - for column in pandas_dataframe.columns: - rich_table.add_column(str(column)) - - for index, value_list in enumerate(pandas_dataframe.values.tolist()): - row = [str(index)] if show_index else [] - row += [str(x) for x in value_list] - rich_table.add_row(*row) - - return rich_table - - -def print_rich_table(df, caption=None): - console = Console() - table = Table(show_header=True, header_style="bold magenta", caption=caption) - table = df_to_table(df, table) - table.row_styles = ["none", "dim"] - table.box = box.SIMPLE_HEAD - table.expand = True - console.print(table) diff --git a/src/blueprinting/core/__init__.py b/src/blueprinting/core/__init__.py deleted file mode 100644 index d9a89f3..0000000 --- a/src/blueprinting/core/__init__.py +++ /dev/null @@ -1,40 +0,0 @@ -"""Blueprinting 核心计算模块 - -符号类型 (均基于 sympy.Function，惰性求值): -- SymPick: 条件表达式 (if-then-else) -- SymMax / SymMin: 惰性最大 / 最小值 - -预定义符号 (按类组织: Model, Parallel, Exec, Hardware): -- 也可扁平导入: from blueprinting.core import BATCH, TP, ... -""" - -from .symbolic import SymMax, SymMin, SymPick, eval_lazy, get_symbol, sym_max, sym_min -from .symbols import ( - ALL_SYMBOLS, - ATTN_HEADS, - BATCH, - CP, - DP, - EP, - EXEC_SYMBOLS, - FEEDFORWARD, - HARDWARE_SYMBOLS, - HEAD_DIM, - HIDDEN, - MEM_BANDWIDTH, - MICRO_BATCH, - MODEL_SYMBOLS, - NET_BANDWIDTH, - NUM_LAYERS, - NUM_MICRO_BATCHES, - PARALLEL_SYMBOLS, - PEAK_FLOPS, - PP, - SEQ, - TP, - VOCAB_SIZE, - Exec, - Hardware, - Model, - Parallel, -) diff --git a/src/blueprinting/core/symbolic.py b/src/blueprinting/core/symbolic.py deleted file mode 100644 index 1e872c0..0000000 --- a/src/blueprinting/core/symbolic.py +++ /dev/null @@ -1,146 +0,0 @@ -"""符号表达式扩展 - -基于 sympy.Function 的惰性求值符号表达式： -- SymPick: 条件表达式 (if-then-else) -- SymMax / SymMin: 惰性 max / min -- eval_lazy: 统一替换求值 -""" - -from typing import Any, Dict - -from sympy import Expr, Function, S, Symbol, sympify - -from .symbols import ALL_SYMBOLS - - -def get_symbol(name: str) -> Symbol: - """根据名称获取预定义符号，不存在则创建新符号.""" - symbol_map = {s.name: s for s in ALL_SYMBOLS} - return symbol_map.get(name, Symbol(name)) - - -class SymPick(Function): - """符号条件表达式 (if-then-else). - - 条件可判定时自动折叠，否则保持惰性。subs() 后自动重新触发 eval。 - - >>> tp = Symbol('tp') - >>> SymPick(tp > 1, tp * 100, 0).subs(tp, 4) - 400 - """ - - nargs = (3,) - - @classmethod - def eval(cls, cond, true_expr, false_expr): - if cond is S.true or cond == S.true: - return true_expr - if cond is S.false or cond == S.false: - return false_expr - try: - return true_expr if bool(cond) else false_expr - except TypeError: - return None - - def _latex(self, printer): - c = printer.doprint(self.args[0]) - t = printer.doprint(self.args[1]) - f = printer.doprint(self.args[2]) - return rf"\begin{{cases}} {t} & \text{{if }} {c} \\ {f} & \text{{otherwise}} \end{{cases}}" - - -class SymMax(Function): - """惰性最大值，全部具体化时折叠，否则保持未求值。 - - 与 sympy.Max 的区别：不触发代数简化。支持嵌套自动展平。 - - >>> a = Symbol('a') - >>> SymMax(a, 10).subs(a, 15) - 15 - """ - - @classmethod - def eval(cls, *args): - flat, changed = [], False - for a in args: - if isinstance(a, SymMax): - flat.extend(a.args) - changed = True - else: - flat.append(a) - if changed: - return cls(*flat) - if all(a.is_number for a in args): - return sympify(max(float(a) for a in args)) - return None - - def _latex(self, printer): - return rf"\max\left({', '.join(printer.doprint(a) for a in self.args)}\right)" - - -class SymMin(Function): - """惰性最小值，与 SymMax 对称。 - - >>> a = Symbol('a') - >>> SymMin(a, 10).subs(a, 5) - 5 - """ - - @classmethod - def eval(cls, *args): - flat, changed = [], False - for a in args: - if isinstance(a, SymMin): - flat.extend(a.args) - changed = True - else: - flat.append(a) - if changed: - return cls(*flat) - if all(a.is_number for a in args): - return sympify(min(float(a) for a in args)) - return None - - def _latex(self, printer): - return rf"\min\left({', '.join(printer.doprint(a) for a in self.args)}\right)" - - -def sym_max(a, b): - """创建惰性 max，纯数值时直接返回 float。""" - if isinstance(a, (int, float)) and isinstance(b, (int, float)): - return max(float(a), float(b)) - return SymMax(sympify(a), sympify(b)) - - -def sym_min(a, b): - """创建惰性 min，纯数值时直接返回 float。""" - if isinstance(a, (int, float)) and isinstance(b, (int, float)): - return min(float(a), float(b)) - return SymMin(sympify(a), sympify(b)) - - -def eval_lazy(expr, subs: dict = None): - """对表达式执行符号替换并尝试数值化. - - Args: - expr: 任意表达式 - subs: 替换字典，key 可以是 Symbol 或 str - """ - if isinstance(expr, (int, float)): - return expr - - subs = subs or {} - normalized: Dict[Symbol, Any] = { - (get_symbol(k) if isinstance(k, str) else k): v for k, v in subs.items() - } - - if isinstance(expr, Expr): - result = expr.subs(normalized) - if result.is_number: - try: - return float(result) - except (TypeError, ValueError): - return result - return result - - return expr diff --git a/src/blueprinting/core/symbols.py b/src/blueprinting/core/symbols.py deleted file mode 100644 index 8894c97..0000000 --- a/src/blueprinting/core/symbols.py +++ /dev/null @@ -1,83 +0,0 @@ -"""预定义符号常量 - -按类别组织仿真建模中的符号，便于引用和批量操作。 - - from blueprinting.core import Model, Parallel, BATCH, TP, ALL_SYMBOLS -""" - -from sympy import Symbol - - -class Model: - """模型结构相关符号.""" - - BATCH = Symbol("batch", positive=True, integer=True) - SEQ = Symbol("seq", positive=True, integer=True) - HIDDEN = Symbol("hidden", positive=True, integer=True) - FEEDFORWARD = Symbol("feedforward", positive=True, integer=True) - NUM_LAYERS = Symbol("num_layers", positive=True, integer=True) - ATTN_HEADS = Symbol("attn_heads", positive=True, integer=True) - HEAD_DIM = Symbol("head_dim", positive=True, integer=True) - VOCAB_SIZE = Symbol("vocab_size", positive=True, integer=True) - - -class Parallel: - """并行策略相关符号.""" - - TP = Symbol("tp", positive=True, integer=True) - PP = Symbol("pp", positive=True, integer=True) - DP = Symbol("dp", positive=True, integer=True) - CP = Symbol("cp", positive=True, integer=True) - EP = Symbol("ep", positive=True, integer=True) - - -class Exec: - """执行 / 调度相关符号.""" - - MICRO_BATCH = Symbol("micro_batch", positive=True, integer=True) - NUM_MICRO_BATCHES = Symbol("num_micro_batches", positive=True, integer=True) - - -class Hardware: - """硬件能力相关符号.""" - - PEAK_FLOPS = Symbol("peak_flops", positive=True) - MEM_BANDWIDTH = Symbol("mem_bandwidth", positive=True) - NET_BANDWIDTH = Symbol("net_bandwidth", positive=True) - - -# 符号集合 -MODEL_SYMBOLS = { - Model.BATCH, - Model.SEQ, - Model.HIDDEN, - Model.FEEDFORWARD, - Model.NUM_LAYERS, - Model.ATTN_HEADS, - Model.HEAD_DIM, - Model.VOCAB_SIZE, -} -PARALLEL_SYMBOLS = {Parallel.TP, Parallel.PP, Parallel.DP, Parallel.CP, Parallel.EP} -EXEC_SYMBOLS = {Exec.MICRO_BATCH, Exec.NUM_MICRO_BATCHES} -HARDWARE_SYMBOLS = {Hardware.PEAK_FLOPS, Hardware.MEM_BANDWIDTH, Hardware.NET_BANDWIDTH} -ALL_SYMBOLS = MODEL_SYMBOLS | PARALLEL_SYMBOLS | EXEC_SYMBOLS | HARDWARE_SYMBOLS - -# 扁平别名 -BATCH = Model.BATCH -SEQ = Model.SEQ -HIDDEN = Model.HIDDEN -FEEDFORWARD = Model.FEEDFORWARD -NUM_LAYERS = Model.NUM_LAYERS -ATTN_HEADS = Model.ATTN_HEADS -HEAD_DIM = Model.HEAD_DIM -VOCAB_SIZE = Model.VOCAB_SIZE -TP = Parallel.TP -PP = Parallel.PP -DP = Parallel.DP -CP = Parallel.CP -EP = Parallel.EP -MICRO_BATCH = Exec.MICRO_BATCH -NUM_MICRO_BATCHES = Exec.NUM_MICRO_BATCHES -PEAK_FLOPS = Hardware.PEAK_FLOPS -MEM_BANDWIDTH = Hardware.MEM_BANDWIDTH -NET_BANDWIDTH = Hardware.NET_BANDWIDTH diff --git a/src/blueprinting/io.py b/src/blueprinting/io.py deleted file mode 100644 index 532a6a3..0000000 --- a/src/blueprinting/io.py +++ /dev/null @@ -1,44 +0,0 @@ -"""IO utilities for blueprinting.""" - -import json -import os -from typing import Any, Dict - -__all__ = ["read_json_file", "write_json_file", "is_json_extension"] - - -def read_json_file(filepath: str) -> Dict[str, Any]: - """Read a JSON file and return its contents. - - Args: - filepath: Path to the JSON file - - Returns: - Dictionary with file contents - """ - with open(filepath) as f: - return json.load(f) - - -def write_json_file(data: Dict[str, Any], filepath: str) -> None: - """Write data to a JSON file. - - Args: - data: Dictionary to write - filepath: Path to the output file - """ - with open(filepath, "w") as f: - json.dump(data, f, indent=2) - - -def is_json_extension(filepath: str) -> bool: - """Check if a filepath has a JSON extension. - - Args: - filepath: Path to check - - Returns: - True if the file has a .json extension - """ - _, ext = os.path.splitext(filepath) - return ext.lower() == ".json" diff --git a/src/blueprinting/nn/__init__.py b/src/blueprinting/nn/__init__.py deleted file mode 100755 index 6fcf1c2..0000000 --- a/src/blueprinting/nn/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -from .base import TensorDef -from .modules import ( - AddDef, - BatchMatmulDef, - ColumnParallelLinear, - LayerNormDef, - LinearDef, - MulDef, - RMSNormDef, - RowParallelLinear, - SequenceParallelAdd, - SequenceParallelRMSNorm, - SiLUDef, - SoftmaxDef, -) diff --git a/src/blueprinting/nn/base.py b/src/blueprinting/nn/base.py deleted file mode 100755 index 2944701..0000000 --- a/src/blueprinting/nn/base.py +++ /dev/null @@ -1,296 +0,0 @@ -from dataclasses import dataclass -from typing import List, Tuple - -import hyperparameter as hp -from sympy import Expr - -from blueprinting.types.base import Calculation, DType, TensorDef - - -@hp.param("sys") -def flops_throughput(dtype, flops, use_matrix_core=True): - if isinstance(flops, Expr): - subs = hp.scope.blueprinting.symbolic.subs | [] - flops = int(flops.subs(dict(subs))) - if use_matrix_core: - throughput = 1e12 * (hp.scope.sys.matrix.float16.tflops | 0) - efficiency = hp.scope.sys.matrix.float16.gflops_efficiency | [] - else: - throughput = 1e12 * (hp.scope.sys.vector.float16.tflops | 0) - efficiency = hp.scope.sys.vector.float16.gflops_efficiency | [] - efficiency_ratio = 1.0 - for gflops, eff in efficiency: - if flops > gflops * 1e9: - efficiency_ratio = eff - break - return throughput * efficiency_ratio - - -@hp.param("sys") -def memrw_throughput(memrw): - if isinstance(memrw, Expr): - subs = hp.scope.blueprinting.symbolic.subs | {} - memrw = int(memrw.subs(dict(subs))) - throughput = 1e9 * (hp.scope.sys.mem1.GBps | 0) - efficiency = hp.scope.sys.mem1.MB_efficiency | [] - efficiency_ratio = 1.0 - for mbytes, eff in efficiency: - if memrw > mbytes * 1e6: - efficiency_ratio = eff - break - return throughput * efficiency_ratio - - -@hp.param("sys") -def c2c_throughput(): - networks = hp.scope.sys.networks | [] - throughput = 1e9 * (networks[0]["bandwidth"]) - efficiency = networks[0]["efficiency"] - return throughput * efficiency - - -@hp.param("sys") -def c2c_nbytes(c2c, comm_type, num_peers): - if isinstance(c2c, Expr): - subs = hp.scope.blueprinting.symbolic.subs | {} - c2c = int(c2c.subs(dict(subs))) - networks = hp.scope.sys.networks | [] - ops = networks[0]["ops"] - op_size = 0 - for op, [scalar, offset] in ops.items(): - if comm_type == op: - op_size = scalar * c2c + offset * (1 / num_peers * c2c) - break - return op_size - - -def c2c_times(comm_type, comm_size, throughput): - networks = hp.scope.sys.networks | [] - ops = networks[0]["ops"] - latency = networks[0]["latency"] - times = comm_size / throughput - return times + latency if comm_type in ops else 0.0 - - -@dataclass -class LayerDef: - dtype: DType - - def forward(self, *inputs) -> TensorDef: - return TensorDef() - - def __call__(self, *inputs: TensorDef): - return Calculation(inputs, self.forward(*inputs), self) - - @property - def nelems(self) -> int: - return 0 - - @property - @hp.param("blueprinting.layerdef") - def nbytes_input(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - return inputs[0].nbytes - - @property - @hp.param("blueprinting.layerdef") - def nbytes_output(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - return self(*inputs).nbytes - - @property - @hp.param("blueprinting.layerdef") - def nbytes_weight(self) -> int: - if not hasattr(self, "weight"): - return 0 - return self.weight.nbytes - - @property - @hp.param("blueprinting.layerdef") - def nbytes_weight_grads(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - if not hasattr(self, "weight"): - return 0 - weight_grads = self.weight.nbytes - return weight_grads - - @property - @hp.param("blueprinting.layerdef") - def nbytes_activity(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - return inputs[0].nbytes - - @property - @hp.param("blueprinting.layerdef") - def nbytes_activity_grads(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - return self(*inputs).nbytes - - @property - @hp.param("blueprinting.layerdef") - def flops_fw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - return 0 - - @property - @hp.param("blueprinting.layerdef") - def flops_bw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - return 0 - - @property - @hp.param("blueprinting.layerdef") - def c2c_fw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - return 0 - - @property - @hp.param("blueprinting.layerdef") - def c2c_bw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - return 0 - - @property - @hp.param("blueprinting.layerdef") - def time_flops_fw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - flops = self.flops_fw - throughput = flops_throughput(self.dtype, flops) - return flops / throughput - - @property - @hp.param("blueprinting.layerdef") - def time_flops_bw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - flops = self.flops_bw - throughput = flops_throughput(self.dtype, flops) - return flops / throughput - - @property - @hp.param("blueprinting.layerdef") - def time_memrw_fw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - memrw = self.memory_fw - throughput = memrw_throughput(memrw) - return memrw / throughput - - @property - @hp.param("blueprinting.layerdef") - def time_memrw_bw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - memrw = self.memory_bw - throughput = memrw_throughput(memrw) - return memrw / throughput - - @property - @hp.param("blueprinting.layerdef") - def time_fw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - time_c2c_fw = self.time_c2c_fw - time_flops = self.time_flops_fw - time_memrw = self.time_memrw_fw - if hp.scope.sys.processing_mode | "roofline" == "roofline": - return max(time_flops, time_memrw) - # hp.scope.sys.processing_mode == "no_overlap" - return time_flops + time_memrw + time_c2c_fw - - @property - @hp.param("blueprinting.layerdef") - def time_bw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - time_flops = self.time_flops_bw - time_memrw = self.time_memrw_bw - if hp.scope.sys.processing_mode | "roofline" == "roofline": - return max(time_flops, time_memrw) - # hp.scope.sys.processing_mode == "no_overlap" - return time_flops + time_memrw - - @property - @hp.param("blueprinting.layerdef") - def memory_fw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - input_memory = self.nbytes_input - output_memory = self.nbytes_output - - if hasattr(self, "weight"): - weight_memory = self.nbytes_weight - return input_memory + weight_memory + output_memory - else: - return input_memory + output_memory - - @property - @hp.param("blueprinting.layerdef") - def wgrad_memory(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - if not hasattr(self, "weight"): - return 0 - wgard_mem = ( - self.nbytes_weight_grads + self.nbytes_activity + self.nbytes_activity_grads - ) - return wgard_mem - - @property - @hp.param("blueprinting.layerdef") - def agrad_memory(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - if hasattr(self, "weight"): - agard_mem = ( - self.nbytes_weight + self.nbytes_activity + self.nbytes_activity_grads - ) - else: - agard_mem = self.memory_fw - - return agard_mem - - @property - @hp.param("blueprinting.layerdef") - def memory_bw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - agrad_memory = self.agrad_memory - wgrad_memory = self.wgrad_memory - - return agrad_memory + wgrad_memory - - @property - @hp.param("blueprinting.layerdef") - def time_c2c_fw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - return 0 - - @property - @hp.param("blueprinting.layerdef") - def time_c2c_bw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - return 0 - - @property - @hp.param("blueprinting.layerdef") - def placement_weight(self, inputs: List[TensorDef] = None) -> Tuple[TensorDef, ...]: - if inputs is None: - inputs = [] - return [TensorDef([0, 0], dtype=self.dtype)] - - @property - def dsize(self) -> int: - return self.dtype.size diff --git a/src/blueprinting/nn/modules.py b/src/blueprinting/nn/modules.py deleted file mode 100755 index 77086e2..0000000 --- a/src/blueprinting/nn/modules.py +++ /dev/null @@ -1,937 +0,0 @@ -from dataclasses import dataclass -from functools import reduce -from operator import mul -from typing import List, Tuple, Union - -import hyperparameter as hp -from sympy import Expr - -from blueprinting.core import SymPick -from blueprinting.nn.base import LayerDef, c2c_nbytes, c2c_throughput, c2c_times -from blueprinting.types.base import DType, TensorDef -from blueprinting.util import pick - - -@hp.param("blueprinting.flops") -def gemm_flops( - A: TensorDef, B: TensorDef, with_bias=False, count_add=True -) -> Union[int, Expr]: - """Calculate flops for GEMM - - Examples - -------- - >>> from sympy import symbols - >>> a, b, c, d, e, f, g = symbols("a b c d e f g") - >>> gemm_flops(TensorDef([a, b]), TensorDef([b, c])) - a*b*c - >>> gemm_flops(TensorDef([a, b]), TensorDef([b, c]), count_add=True) - a*b*(2*c - 1) - >>> gemm_flops(TensorDef([a, b]), TensorDef([b, c]), with_bias=True, count_add=True) - 2*a*b*c - >>> gemm_flops(TensorDef([a, b, c]), TensorDef([c, d])) - a*b*c*d - >>> gemm_flops(TensorDef([a, b, c]), TensorDef([c, d]), count_add=True) - a*b*c*(2*d - 1) - >>> gemm_flops(TensorDef([a, b, c]), TensorDef([c, d]), with_bias=True, count_add=True) - 2*a*b*c*d - >>> gemm_flops(TensorDef([a, b, c]), TensorDef([c, b, d])) - a*b*c*d - >>> gemm_flops(TensorDef([a, b, c]), TensorDef([c, b, d]), count_add=True) - a*b*c*(2*d - 1) - >>> gemm_flops(TensorDef([a, b, c]), TensorDef([c, b, d]), with_bias=True, count_add=True) - 2*a*b*c*d - """ - if A.shape[-1] != B.shape[0]: - raise Exception(f"bad gemm: {A} x {B}: {A.shape[-1]} != {B.shape[0]}") - offset = len(B.shape) - 1 - for idx, (a, b) in enumerate(zip(reversed(A.shape), B.shape[:-1])): - if a != b: - offset = idx - break - - if count_add: - if with_bias: - return reduce(mul, A.shape) * 2 * reduce(mul, B.shape[offset:]) - return reduce(mul, A.shape) * (2 * reduce(mul, B.shape[offset:]) - 1) - return reduce(mul, A.shape) * reduce(mul, B.shape[offset:]) - - -@hp.param("blueprinting.flops") -def batch_gemm_flops( - A: TensorDef, B: TensorDef, with_bias=False, count_add=True -) -> Union[int, Expr]: - if A.shape[-1] != B.shape[-2]: - raise Exception(f"bad batch_gemm: {A} x {B}: {A.shape[-1]} != {B.shape[-2]}") - - if count_add: - if with_bias: - return reduce(mul, A.shape) * 2 * B.shape[-1] - return reduce(mul, A.shape) * (2 * (B.shape[-1]) - 1) - return reduce(mul, A.shape) * B.shape[-1] - - -@dataclass -class LinearDef(LayerDef): - dtype: DType - in_features: Union[int, Expr] - out_features: Union[int, Expr] - bias: bool = True - - def __post_init__(self): - self.weight = TensorDef((self.in_features, self.out_features), dtype=self.dtype) - - def forward(self, *inputs) -> TensorDef: - return TensorDef(inputs[0].shape[:-1] + [self.out_features], inputs[0].dtype) - - @property - def nelems(self): - w = self.weight.nelems - return w + self.out_features if self.bias else w - - @property - @hp.param("blueprinting.layerdef") - def flops_fw(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - if not inputs: - return 0 - return gemm_flops(inputs[0], self.weight, with_bias=self.bias) - - @property - @hp.param("blueprinting.layerdef") - def flops_bw(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - if not inputs: - return 0 - grad_output = self(*inputs).belike() - act_flops = gemm_flops(grad_output, self.weight.T, with_bias=self.bias) - w_flops = gemm_flops(inputs[0].T, grad_output, with_bias=self.bias) - return act_flops + w_flops - - -@dataclass -class ColumnParallelLinear(LinearDef): - tensor_model_parallel_size: int = 1 - tensor_par_comm_type: str = "ar" - - def __post_init__(self): - self.weight = TensorDef( - (self.in_features, self.out_features / self.tensor_model_parallel_size), - dtype=self.dtype, - ) - - def forward(self, *inputs) -> TensorDef: - return TensorDef( - inputs[0].shape[:-1] - + [self.out_features / self.tensor_model_parallel_size], - inputs[0].dtype, - ) - - @property - @hp.param("blueprinting.layerdef") - def flops_bw(self, inputs: List[TensorDef] = None): - """ColumnParallelLinear backward FLOPs (不包含通信开销)""" - if inputs is None: - inputs = [] - if not inputs: - return 0 - grad_output = self(*inputs).belike() - act_flops = gemm_flops(grad_output, self.weight.T, with_bias=self.bias) - w_flops = gemm_flops(inputs[0].T, grad_output, with_bias=self.bias) - # 注意: all_reduce 是通信开销，不应计入 FLOPs - return act_flops + w_flops - - @property - @hp.param("blueprinting.layerdef") - def c2c_fw(self, inputs: List[TensorDef] = None) -> int: - """ColumnParallelLinear forward c2c: rs_ag 模式下才有通信""" - if inputs is None: - inputs = [] - if not inputs: - return 0 - nbytes = self.forward(*inputs).nbytes - # rs_ag 模式且 TP>1 时有 all_gather 通信 - if self.tensor_par_comm_type == "rs_ag": - return SymPick(self.tensor_model_parallel_size > 1, nbytes, 0) - return 0 - - @property - @hp.param("blueprinting.layerdef") - def c2c_bw(self, inputs: List[TensorDef] = None) -> int: - """ColumnParallelLinear backward c2c: TP>1 时有通信""" - if inputs is None: - inputs = [] - if not inputs: - return 0 - nbytes = self.forward(*inputs).nbytes - return SymPick(self.tensor_model_parallel_size > 1, nbytes, 0) - - @property - @hp.param("blueprinting.layerdef") - def time_c2c_fw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - c2c = self.c2c_fw - comm_type = pick(self.tensor_par_comm_type == "rs_ag", "all_gather", "identity") - throughput = c2c_throughput() - comm_size = c2c_nbytes(c2c, comm_type, self.tensor_model_parallel_size) - return c2c_times(comm_type, comm_size, throughput) - - @property - @hp.param("blueprinting.layerdef") - def time_c2c_bw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - c2c = self.c2c_bw - comm_type = pick( - self.tensor_par_comm_type == "rs_ag", "reduce_scatter", "all_reduce" - ) - throughput = c2c_throughput() - comm_size = c2c_nbytes(c2c, comm_type, self.tensor_model_parallel_size) - return c2c_times(comm_type, comm_size, throughput) - - @property - @hp.param("blueprinting.layerdef") - def placement_weight(self, inputs: List[TensorDef] = None) -> Tuple[TensorDef, ...]: - if inputs is None: - inputs = [] - row = self.in_features - column = self.out_features / self.tensor_model_parallel_size - return [TensorDef([row, column], dtype=self.dtype)] - - -@dataclass -class RowParallelLinear(LinearDef): - tensor_model_parallel_size: int = 1 - tensor_par_comm_type: str = "ar" - - def __post_init__(self): - self.weight = TensorDef( - (self.in_features / self.tensor_model_parallel_size, self.out_features), - dtype=self.dtype, - ) - - def forward(self, *inputs) -> TensorDef: - return TensorDef(inputs[0].shape[:-1] + [self.out_features], inputs[0].dtype) - - @property - @hp.param("blueprinting.layerdef") - def nbytes_input(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - input = TensorDef( - inputs[0].shape[:-1] + [self.in_features / self.tensor_model_parallel_size], - inputs[0].dtype, - ) - return input.nbytes - - @property - @hp.param("blueprinting.layerdef") - def nbytes_activity(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - input = TensorDef( - inputs[0].shape[:-1] + [self.in_features / self.tensor_model_parallel_size], - inputs[0].dtype, - ) - return input.nbytes - - @property - @hp.param("blueprinting.layerdef") - def flops_fw(self, inputs: List[TensorDef] = None) -> int: - """RowParallelLinear forward FLOPs (不包含通信开销)""" - if inputs is None: - inputs = [] - input = TensorDef( - inputs[0].shape[:-1] + [self.in_features / self.tensor_model_parallel_size], - inputs[0].dtype, - ) - output_flops = gemm_flops(input, self.weight, with_bias=self.bias) - # 注意: all_reduce 是通信开销，不应计入 FLOPs - return output_flops - - @property - @hp.param("blueprinting.layerdef") - def flops_bw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - if not inputs: - return 0 - input = TensorDef( - inputs[0].shape[:-1] + [self.in_features / self.tensor_model_parallel_size], - inputs[0].dtype, - ) - grad_output = self(*inputs).belike() - act_flops = gemm_flops(grad_output, self.weight.T, with_bias=self.bias) - w_flops = gemm_flops(input.T, grad_output, with_bias=self.bias) - return act_flops + w_flops - - @property - @hp.param("blueprinting.layerdef") - def c2c_fw(self, inputs: List[TensorDef] = None) -> int: - """RowParallelLinear forward c2c: TP>1 时有 all_reduce/reduce_scatter 通信""" - if inputs is None: - inputs = [] - if not inputs: - return 0 - nbytes = self.forward(*inputs).nbytes - return SymPick(self.tensor_model_parallel_size > 1, nbytes, 0) - - @property - @hp.param("blueprinting.layerdef") - def c2c_bw(self, inputs: List[TensorDef] = None) -> int: - """RowParallelLinear backward c2c: rs_ag 模式下才有通信""" - if inputs is None: - inputs = [] - if not inputs: - return 0 - nbytes = self.forward(*inputs).nbytes - # rs_ag 模式且 TP>1 时有 all_gather 通信 - if self.tensor_par_comm_type == "rs_ag": - return SymPick(self.tensor_model_parallel_size > 1, nbytes, 0) - return 0 - - @property - @hp.param("blueprinting.layerdef") - def time_c2c_fw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - c2c = self.c2c_fw - comm_type = pick( - self.tensor_par_comm_type == "rs_ag", "reduce_scatter", "all_reduce" - ) - throughput = c2c_throughput() - comm_size = c2c_nbytes(c2c, comm_type, self.tensor_model_parallel_size) - return c2c_times(comm_type, comm_size, throughput) - - @property - @hp.param("blueprinting.layerdef") - def time_c2c_bw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - c2c = self.c2c_bw - comm_type = pick(self.tensor_par_comm_type == "rs_ag", "all_gather", "identity") - throughput = c2c_throughput() - comm_size = c2c_nbytes(c2c, comm_type, self.tensor_model_parallel_size) - return c2c_times(comm_type, comm_size, throughput) - - @property - @hp.param("blueprinting.layerdef") - def placement_weight(self, inputs: List[TensorDef] = None) -> Tuple[TensorDef, ...]: - if inputs is None: - inputs = [] - row = self.in_features / self.tensor_model_parallel_size - column = self.out_features - return [TensorDef([row, column], dtype=self.dtype)] - - -@dataclass -class LayerNormDef(LayerDef): - dtype: DType - normalized_shape: List[Union[int, Expr]] - bias: bool = True - - def __post_init__(self): - self.weight = TensorDef(self.normalized_shape, dtype=self.dtype) - - def forward(self, *inputs) -> TensorDef: - return TensorDef(inputs[0].shape, inputs[0].dtype) - - @property - def nelems(self) -> int: - return self.normalized_shape * 2 if self.bias else self.normalized_shape - - @property - @hp.param("blueprinting.layerdef") - def nbytes_input(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - return inputs[0].nbytes - - @property - @hp.param("blueprinting.layerdef") - def nbytes_output(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - return self(*inputs).nbytes - - @property - @hp.param("blueprinting.layerdef") - def nbytes_activity_grads(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - return inputs[0].nbytes - - @property - @hp.param("blueprinting.layerdef") - def nbytes_weight(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - return self.normalized_shape * 2 * self.dsize - - @property - @hp.param("blueprinting.layerdef") - def nbytes_weight_grads(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - return self.normalized_shape * 2 * self.dsize - - @property - @hp.param("blueprinting.layerdef") - def flops_fw(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - if not inputs: - return 0 - num_features = reduce(mul, inputs[0].shape) - mean_flops = num_features - var_flops = 2 * num_features - offset_flops = num_features - scale_flops = num_features - bias_flops = num_features if self.bias else 0 - return mean_flops + var_flops + offset_flops + scale_flops + bias_flops - - @property - @hp.param("blueprinting.layerdef") - def flops_bw(self, inputs: List[TensorDef] = None): - """LayerNorm backward: 涉及 mean/var 梯度计算，约 10-12 ops per element - - 参考: https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html - """ - if inputs is None: - inputs = [] - if not inputs: - return 0 - num_features = reduce(mul, inputs[0].shape) - # 反向传播涉及: dx_hat, dvar, dmean, dx, dgamma, dbeta - # 总计约 10-12 ops per element - return 10 * num_features - - @property - @hp.param("blueprinting.layerdef") - def placement_weight(self, inputs: List[TensorDef] = None) -> Tuple[TensorDef, ...]: - if inputs is None: - inputs = [] - return [ - TensorDef([self.normalized_shape], dtype=self.dtype), - TensorDef([self.normalized_shape], dtype=self.dtype), - ] - - -@dataclass -class Conv2dDef(LayerDef): - dtype: str - in_channels: int - out_channels: int - - -""" - https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html - https://cthorey.github.io./blog/2016/backpropagation/ -""" - - -@dataclass -class RMSNormDef(LayerDef): - """RMSNorm: Root Mean Square Layer Normalization - - Forward: y = x / sqrt(mean(x^2) + eps) * gamma - Backward: 需要计算 dx 和 dgamma - """ - - dtype: DType - normalized_shape: List[Union[int, Expr]] - eps: float = 1e-5 - elementwise_affine: bool = True - - def forward(self, *inputs): - return TensorDef(inputs[0].shape, inputs[0].dtype) - - @property - def nelems(self): - return self.normalized_shape - - @property - @hp.param("blueprinting.layerdef") - def flops_fw(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - if not inputs: - return 0 - num_features = reduce(mul, inputs[0].shape) - square_flops = num_features # 平方运算 - mean_flops = num_features - div_flops = num_features - scale_flops = num_features - return square_flops + div_flops + mean_flops + scale_flops - - @property - @hp.param("blueprinting.layerdef") - def flops_bw(self, inputs: List[TensorDef] = None): - """RMSNorm backward: ~8 ops per element for gradient computation""" - if inputs is None: - inputs = [] - if not inputs: - return 0 - num_features = reduce(mul, inputs[0].shape) - # Backward pass involves: computing input gradients and weight gradients - # Similar complexity to forward, approximately 2x forward - return 8 * num_features - - @property - @hp.param("blueprinting.layerdef") - def placement_weight(self, inputs: List[TensorDef] = None) -> Tuple[TensorDef, ...]: - if inputs is None: - inputs = [] - return [TensorDef([self.normalized_shape], dtype=self.dtype)] - - -@dataclass -class SequenceParallelRMSNorm(RMSNormDef): - tensor_model_parallel_size: int = 1 - - def forward(self, *inputs): - seq_len = inputs[0].shape[-2] / self.tensor_model_parallel_size - return TensorDef( - inputs[0].shape[:-2] + [seq_len, inputs[0].shape[-1]], inputs[0].dtype - ) - - @property - @hp.param("blueprinting.layerdef") - def nbytes_input(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - seq_len = inputs[0].shape[-2] / self.tensor_model_parallel_size - input = TensorDef( - inputs[0].shape[:-2] + [seq_len, inputs[0].shape[-1]], inputs[0].dtype - ) - return input.nbytes - - @property - @hp.param("blueprinting.layerdef") - def nbytes_activity(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - seq_len = inputs[0].shape[-2] / self.tensor_model_parallel_size - input = TensorDef( - inputs[0].shape[:-2] + [seq_len, inputs[0].shape[-1]], inputs[0].dtype - ) - return input.nbytes - - @property - @hp.param("blueprinting.layerdef") - def flops_fw(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - if not inputs: - return 0 - seq_len = inputs[0].shape[-2] / self.tensor_model_parallel_size - input = TensorDef( - inputs[0].shape[:-2] + [seq_len, inputs[0].shape[-1]], inputs[0].dtype - ) - num_features = reduce(mul, input.shape) - square_flops = num_features # 平方运算 - mean_flops = num_features - div_flops = num_features - scale_flops = num_features - return square_flops + div_flops + mean_flops + scale_flops - - @property - @hp.param("blueprinting.layerdef") - def flops_bw(self, inputs: List[TensorDef] = None): - """SequenceParallelRMSNorm backward""" - if inputs is None: - inputs = [] - if not inputs: - return 0 - seq_len = inputs[0].shape[-2] / self.tensor_model_parallel_size - input = TensorDef( - inputs[0].shape[:-2] + [seq_len, inputs[0].shape[-1]], inputs[0].dtype - ) - num_features = reduce(mul, input.shape) - return 8 * num_features - - @property - @hp.param("blueprinting.layerdef") - def placement_weight(self, inputs: List[TensorDef] = None) -> Tuple[TensorDef, ...]: - if inputs is None: - inputs = [] - return [TensorDef([self.normalized_shape], dtype=self.dtype)] - - -""" - https://pytorch.org/docs/stable/generated/torch.bmm.html -""" - - -@dataclass -class BatchMatmulDef(LayerDef): - """Batch Matrix Multiplication: torch.bmm - - 无权重层，需要两个输入张量进行批量矩阵乘法。 - """ - - dtype: DType - - def forward(self, *inputs): - return TensorDef(inputs[0].shape[:-1] + [inputs[1].shape[-1]], inputs[0].dtype) - - @property - @hp.param("blueprinting.layerdef") - def nbytes_activity(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - if len(inputs) < 2: - return 0 - return ( - reduce(mul, inputs[0].shape) + reduce(mul, inputs[1].shape) - ) * self.dsize - - @property - @hp.param("blueprinting.layerdef") - def nbytes_activity_grads(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - if len(inputs) < 2: - return 0 - activity_grads = TensorDef( - inputs[0].shape[:-1] + [inputs[1].shape[-1]], inputs[0].dtype - ) - return reduce(mul, activity_grads.shape) * self.dsize - - @property - @hp.param("blueprinting.layerdef") - def flops_fw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - if len(inputs) < 2: - return 0 - return batch_gemm_flops(inputs[0], inputs[1]) - - @property - @hp.param("blueprinting.layerdef") - def flops_bw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - if len(inputs) < 2: - return 0 - return 2 * batch_gemm_flops(inputs[0], inputs[1]) - - -""" - https://automata88.medium.com/how-to-implement-the-softmax-derivative-independently-from-any-loss-function-ae6d44363a9d - https://pytorch.org/docs/stable/generated/torch.nn.Softmax.html#torch.nn.Softmax -""" - - -@dataclass -class SoftmaxDef(LayerDef): - dtype: DType - dims: int = ( - 0 # A dimension along which Softmax will be computed (so every slice along dim will sum to 1). - ) - - def forward(self, *inputs): - return TensorDef(inputs[0].shape, inputs[0].dtype) - - @property - @hp.param("blueprinting.layerdef") - def nbytes_activity(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - return reduce(mul, inputs[0].shape) * self.dsize - - @property - @hp.param("blueprinting.layerdef") - def nbytes_activity_grads(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - return reduce(mul, inputs[0].shape) * self.dsize - - @property - @hp.param("blueprinting.layerdef") - def flops_fw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - if not inputs: - return 0 - return 5 * reduce(mul, inputs[0].shape) - - @property - @hp.param("blueprinting.layerdef") - def flops_bw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - if not inputs: - return 0 - return 8 * reduce(mul, inputs[0].shape) - - -""" - https://pytorch.org/docs/stable/generated/torch.nn.SiLU.html#torch.nn.SiLU -""" - - -@dataclass -class SiLUDef(LayerDef): - """SiLU (Swish) activation: x * sigmoid(x) - - FLOPs计算: - - Forward: sigmoid(x) 需要 exp + div + 1 = 3 ops，乘法 1 op，共 4 ops per element - - Backward: d/dx[x * sigmoid(x)] = sigmoid(x) + x * sigmoid(x) * (1 - sigmoid(x)) - 需要约 6 ops per element - """ - - dtype: DType - inplace: bool = False - - def forward(self, *inputs): - return TensorDef(inputs[0].shape, inputs[0].dtype) - - @property - @hp.param("blueprinting.layerdef") - def nbytes_activity(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - return reduce(mul, inputs[0].shape) * self.dsize - - @property - @hp.param("blueprinting.layerdef") - def nbytes_input(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - return inputs[0].nbytes - - @property - @hp.param("blueprinting.layerdef") - def nbytes_output(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - return self(*inputs).nbytes - - @property - @hp.param("blueprinting.layerdef") - def nbytes_activity_grads(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - if not inputs: - return 0 - return reduce(mul, inputs[0].shape) * self.dsize - - @property - @hp.param("blueprinting.layerdef") - def flops_fw(self, inputs: List[TensorDef] = None) -> int: - """SiLU forward: x * sigmoid(x), ~4 ops per element""" - if inputs is None: - inputs = [] - if not inputs: - return 0 - return 4 * reduce(mul, inputs[0].shape) - - @property - @hp.param("blueprinting.layerdef") - def flops_bw(self, inputs: List[TensorDef] = None) -> int: - """SiLU backward: ~6 ops per element for gradient computation""" - if inputs is None: - inputs = [] - if not inputs: - return 0 - return 6 * reduce(mul, inputs[0].shape) - - -""" - https://pytorch.org/docs/stable/generated/torch.add.html#torch.add - https://explained.ai/matrix-calculus/#sec:1.4.2 -""" - - -@dataclass -class AddDef(LayerDef): - """Element-wise addition: torch.add - - 无权重层，执行两个张量的逐元素加法。 - """ - - alpha: int = 1 - - def forward(self, *inputs): - return TensorDef(inputs[0].shape, inputs[0].dtype) - - @property - @hp.param("blueprinting.layerdef") - def nbytes_activity(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - if len(inputs) < 2: - return 0 - return ( - reduce(mul, inputs[0].shape) + reduce(mul, inputs[1].shape) - ) * self.dsize - - @property - @hp.param("blueprinting.layerdef") - def nbytes_input(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - if len(inputs) < 2: - return 0 - return inputs[0].nbytes + inputs[1].nbytes - - @property - @hp.param("blueprinting.layerdef") - def nbytes_output(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - if not inputs: - return 0 - return self.forward(*inputs).nbytes - - @property - @hp.param("blueprinting.layerdef") - def flops_fw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - if not inputs: - return 0 - return reduce(mul, inputs[0].shape) - - @property - @hp.param("blueprinting.layerdef") - def flops_bw(self, inputs: List[TensorDef] = None) -> int: - """加法反向传播: dy/dx = 1, 梯度直接传递，无计算""" - if inputs is None: - inputs = [] - return 0 - - -@dataclass -class MulDef(LayerDef): - """Element-wise multiplication: torch.mul - - 无权重层，执行两个张量的逐元素乘法。 - 用于 SwiGLU 中的 silu(gate) * up 操作。 - """ - - def forward(self, *inputs): - return TensorDef(inputs[0].shape, inputs[0].dtype) - - @property - @hp.param("blueprinting.layerdef") - def nbytes_activity(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - if len(inputs) < 2: - return 0 - return ( - reduce(mul, inputs[0].shape) + reduce(mul, inputs[1].shape) - ) * self.dsize - - @property - @hp.param("blueprinting.layerdef") - def nbytes_input(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - if len(inputs) < 2: - return 0 - return inputs[0].nbytes + inputs[1].nbytes - - @property - @hp.param("blueprinting.layerdef") - def nbytes_output(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - if not inputs: - return 0 - return self.forward(*inputs).nbytes - - @property - @hp.param("blueprinting.layerdef") - def flops_fw(self, inputs: List[TensorDef] = None) -> int: - """逐元素乘法: N 次乘法操作""" - if inputs is None: - inputs = [] - if not inputs: - return 0 - return reduce(mul, inputs[0].shape) - - @property - @hp.param("blueprinting.layerdef") - def flops_bw(self, inputs: List[TensorDef] = None) -> int: - """乘法反向传播: d(x*y)/dx = y, d(x*y)/dy = x - - 需要计算两个梯度，各需要 N 次乘法 - """ - if inputs is None: - inputs = [] - if len(inputs) < 2: - return 0 - return 2 * reduce(mul, inputs[0].shape) - - -@dataclass -class SequenceParallelAdd(AddDef): - """Sequence Parallel Add: 序列并行下的加法操作 - - 在序列并行模式下，序列维度被分割到多个设备上。 - """ - - tensor_model_parallel_size: int = 1 - - def forward(self, *inputs): - seq_len = inputs[0].shape[-2] / self.tensor_model_parallel_size - output = TensorDef( - inputs[0].shape[:-2] + [seq_len, inputs[0].shape[-1]], inputs[0].dtype - ) - return output - - def _get_partitioned_inputs(self, inputs): - """获取序列并行分区后的输入""" - if len(inputs) < 2: - return None, None - seq_len = inputs[0].shape[-2] / self.tensor_model_parallel_size - input0 = TensorDef( - inputs[0].shape[:-2] + [seq_len, inputs[0].shape[-1]], inputs[0].dtype - ) - input1 = TensorDef( - inputs[1].shape[:-2] + [seq_len, inputs[1].shape[-1]], inputs[1].dtype - ) - return input0, input1 - - @property - @hp.param("blueprinting.layerdef") - def nbytes_activity(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - input0, input1 = self._get_partitioned_inputs(inputs) - if input0 is None: - return 0 - return (reduce(mul, input0.shape) + reduce(mul, input1.shape)) * self.dsize - - @property - @hp.param("blueprinting.layerdef") - def nbytes_input(self, inputs: List[TensorDef] = None): - if inputs is None: - inputs = [] - input0, input1 = self._get_partitioned_inputs(inputs) - if input0 is None: - return 0 - return input0.nbytes + input1.nbytes - - @property - @hp.param("blueprinting.layerdef") - def flops_fw(self, inputs: List[TensorDef] = None) -> int: - if inputs is None: - inputs = [] - input0, _ = self._get_partitioned_inputs(inputs) - if input0 is None: - return 0 - return reduce(mul, input0.shape) - - @property - @hp.param("blueprinting.layerdef") - def flops_bw(self, inputs: List[TensorDef] = None) -> int: - """序列并行加法反向传播: dy/dx = 1, 梯度直接传递，无计算""" - if inputs is None: - inputs = [] - return 0 diff --git a/src/blueprinting/st/__init__.py b/src/blueprinting/st/__init__.py deleted file mode 100755 index 2f6b65e..0000000 --- a/src/blueprinting/st/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .blocks.attn import attention_block -from .blocks.container import block -from .blocks.ffn import ffn_block diff --git a/src/blueprinting/st/blocks/__init__.py b/src/blueprinting/st/blocks/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/src/blueprinting/st/blocks/attn.py b/src/blueprinting/st/blocks/attn.py deleted file mode 100755 index 8e3e8ba..0000000 --- a/src/blueprinting/st/blocks/attn.py +++ /dev/null @@ -1,149 +0,0 @@ -import hyperparameter as hp -import streamlit as st -from streamlit_extras.row import row -from sympy import Symbol - -from blueprinting.nn import ( - AddDef, - BatchMatmulDef, - ColumnParallelLinear, - RMSNormDef, - RowParallelLinear, - SequenceParallelAdd, - SequenceParallelRMSNorm, - SoftmaxDef, - TensorDef, -) -from blueprinting.st.blocks.container import block -from blueprinting.st.blocks.formatter import DefaultFormatter, auto_symbol -from blueprinting.types.base import DType -from blueprinting.util import pick - - -@hp.param("model") -def attention_block(): - """Attention 模块计算 - - 参考llama2中的Attention模块 - """ - # 读取hyperparameter的参数配置， 创建 数值:符号 对 - dtype = DType(hp.scope.exe.datatype | "float16") - _seqlen, SEQLEN = hp.scope.model.seq_size | 4096, Symbol("seqlen") - _bsize, BSIZE = hp.scope.exe.microbatch_size | 0, Symbol("bsize") - _hidden, HIDDEN = hp.scope.model.hidden | 4096, Symbol("hidden") - _attnheads, ATTNHEADS = hp.scope.model.attn_heads | 32, Symbol("attnheads") - _attnsize, ATTNSIZE = ( - hp.scope.model.attn_size | 128, - Symbol("attnsize"), - ) # hidden // attn_heads - _tpsize, TPSIZE = hp.scope.exe.tensor_par | 1, Symbol("tpsize") - - tensor_par_comm_type = ( - hp.scope.exe.tensor_par_comm_type | "ar" - ) # "ar" 表示 all reduce， "rs_ag" 表示 reduce scatter and all gather - sequence_par = hp.scope.exe.sequence_par | "true" - - # 创建层级容器 - with block("attn_block", 4): - st.markdown("#### Attention Block") - - with block("attn_res_block", 2): - st.markdown("#### Residual Connection") - res_layer = pick( - sequence_par, - SequenceParallelAdd(dtype, tensor_model_parallel_size=TPSIZE), - AddDef(dtype), - ) - res_out = res_layer( - TensorDef([BSIZE, SEQLEN, HIDDEN], dtype=dtype), - TensorDef([BSIZE, SEQLEN, HIDDEN], dtype=dtype), - ) - res_out | DefaultFormatter.with_subs(auto_symbol()) - - with block("attn_linear_block", 2): - st.markdown("#### Output Projection [Linear]") - linear_layer = RowParallelLinear( - dtype, HIDDEN, HIDDEN, False, TPSIZE, tensor_par_comm_type - ) - linear_out = linear_layer(TensorDef([BSIZE, SEQLEN, HIDDEN], dtype=dtype)) - linear_out | DefaultFormatter.with_subs(auto_symbol()) - - with block("attn_mha_block", 2): - st.markdown("#### Multi-Head Attention") - # TP 切分后，每个设备上的 heads 数量 - ATTNHEADS_PER_TP = ATTNHEADS / TPSIZE - - with block("attn_batch_matmul_block_1", 2): - st.markdown("#### Score @ V [BatchMatmul]") - batchmatmul_layer = BatchMatmulDef(dtype) - # score: [B, heads/TP, S, S], V: [B, heads/TP, S, size] - # output: [B, heads/TP, S, size] - batchmatmul_out = batchmatmul_layer( - TensorDef([BSIZE, ATTNHEADS_PER_TP, SEQLEN, SEQLEN], dtype=dtype), - TensorDef([BSIZE, ATTNHEADS_PER_TP, SEQLEN, ATTNSIZE], dtype=dtype), - ) - batchmatmul_out | DefaultFormatter.with_subs(auto_symbol()) - - with block("attn_softmax_block", 2): - st.markdown("#### QK Score [Softmax]") - softmax_layer = SoftmaxDef(dtype) - softmax_out = softmax_layer( - TensorDef([BSIZE, ATTNHEADS_PER_TP, SEQLEN, SEQLEN], dtype=dtype) - ) - softmax_out | DefaultFormatter.with_subs(auto_symbol()) - - with block("attn_batch_matmul_block_2", 2): - st.markdown("#### Q @ K^T [BatchMatmul]") - batchmatmul_layer = BatchMatmulDef(dtype) - # Q: [B, heads/TP, S, size], K^T: [B, heads/TP, size, S] - # output: [B, heads/TP, S, S] - batchmatmul_out = batchmatmul_layer( - TensorDef([BSIZE, ATTNHEADS_PER_TP, SEQLEN, ATTNSIZE], dtype=dtype), - TensorDef([BSIZE, ATTNHEADS_PER_TP, ATTNSIZE, SEQLEN], dtype=dtype), - ) - batchmatmul_out | DefaultFormatter.with_subs(auto_symbol()) - - r = row(3) - - with r.container(), block("attn_query_block", 2): - st.markdown("#### Query Projection [Linear]") - - q_layer = ColumnParallelLinear( - dtype, HIDDEN, HIDDEN, False, TPSIZE, tensor_par_comm_type - ) - - q_proj = q_layer(TensorDef(shape=[BSIZE, SEQLEN, HIDDEN], dtype=dtype)) - - q_proj | DefaultFormatter.with_subs(auto_symbol()) - - with r.container(), block("attn_key_block", 2): - st.markdown("#### Key Projection [Linear]") - k_layer = ColumnParallelLinear( - dtype, HIDDEN, HIDDEN, False, TPSIZE, tensor_par_comm_type - ) - - k_proj = k_layer(TensorDef(shape=[BSIZE, SEQLEN, HIDDEN], dtype=dtype)) - - k_proj | DefaultFormatter.with_subs(auto_symbol()) - - with r.container(), block("attn_value_block", 2): - st.markdown("#### Value Projection [Linear]") - v_layer = ColumnParallelLinear( - dtype, HIDDEN, HIDDEN, False, TPSIZE, tensor_par_comm_type - ) - - v_proj = v_layer(TensorDef(shape=[BSIZE, SEQLEN, HIDDEN], dtype=dtype)) - - v_proj | DefaultFormatter.with_subs(auto_symbol()) - - with block("attn_rms_block", 2): - st.markdown("#### PreNorm [RMSNorm]") - rms_layer = pick( - sequence_par, - SequenceParallelRMSNorm( - dtype, HIDDEN, tensor_model_parallel_size=TPSIZE - ), - RMSNormDef(dtype, HIDDEN), - ) - rms_out = rms_layer(TensorDef([BSIZE, SEQLEN, HIDDEN], dtype=dtype)) - rms_out | DefaultFormatter.with_subs(auto_symbol()) diff --git a/src/blueprinting/st/blocks/container.py b/src/blueprinting/st/blocks/container.py deleted file mode 100755 index 1f2f818..0000000 --- a/src/blueprinting/st/blocks/container.py +++ /dev/null @@ -1,14 +0,0 @@ -from streamlit_extras.stylable_container import stylable_container - - -def block(key: str, border: int): - return stylable_container( - key, - css_styles=f""" - {{ - border: {border}px solid rgba(49, 51, 63, 0.2); - border-radius: 0.5rem; - padding: calc(1em - 1px) - }} - """, - ) diff --git a/src/blueprinting/st/blocks/ffn.py b/src/blueprinting/st/blocks/ffn.py deleted file mode 100755 index 944e5d6..0000000 --- a/src/blueprinting/st/blocks/ffn.py +++ /dev/null @@ -1,127 +0,0 @@ -import hyperparameter as hp -import streamlit as st -from streamlit_extras.row import row -from sympy import Symbol - -from blueprinting.nn import ( - AddDef, - ColumnParallelLinear, - MulDef, - RMSNormDef, - RowParallelLinear, - SequenceParallelAdd, - SequenceParallelRMSNorm, - SiLUDef, - TensorDef, -) -from blueprinting.st.blocks.container import block -from blueprinting.st.blocks.formatter import DefaultFormatter, auto_symbol -from blueprinting.types.base import DType -from blueprinting.util import pick - - -@hp.param("model") -def ffn_block(hidden=1024, feedforward=1024): - """FFN 模块计算 - - 使用sympy的符号化计算，方便追踪计算过程： - - 通过 `hp.param` 将全局超参中的`model.hidden`与`model.feedforward`映射给`hidden`和`feedforward` - - 创建参数的（数值、符号）对，比如（hidden、HIDDEN） - - 运算过程使用符号进行计算，但输出时同时显示数值与符号 - """ - - # 读取hyperparameter配置，并创建 数值<->符号 对 - dtype = DType(hp.scope.exe.datatype | "float16") - seqlen = hp.scope.model.seq_size | 64 - _bsize, BSIZE = hp.scope.exe.microbatch_size | 0, Symbol("bsize") - seqlen, SEQLEN = seqlen, Symbol("seqlen") - hidden, HIDDEN = hidden, Symbol("hidden") - feedforward, FEEDFORWARD = feedforward, Symbol("feedforward") - _tpsize, TPSIZE = hp.scope.exe.tensor_par | 1, Symbol("tpsize") - - tensor_par_comm_type = ( - hp.scope.exe.tensor_par_comm_type | "ar" - ) # "ar" 表示 all reduce， "rs_ag" 表示 reduce scatter and all gather - sequence_par = hp.scope.exe.sequence_par | False - - # 创建层级容器 - with block("ffn_block", 4): - st.markdown("#### Feed Forward Block") - - with block("ffn_res_block", 2): - st.markdown("#### Residual Connection") - res_layer = pick( - sequence_par, - SequenceParallelAdd(dtype, tensor_model_parallel_size=TPSIZE), - AddDef(dtype), - ) - res_out = res_layer( - TensorDef([BSIZE, SEQLEN, HIDDEN], dtype=dtype), - TensorDef([BSIZE, SEQLEN, HIDDEN], dtype=dtype), - ) - res_out | DefaultFormatter.with_subs(auto_symbol()) - - with block("ffn_pro_block", 2): - st.markdown("##### Down Projection [Linear]") - - # 创建输出层的定义 - output_layer = RowParallelLinear( - dtype, FEEDFORWARD, HIDDEN, False, TPSIZE, tensor_par_comm_type - ) - - # 进行计算 - output_proj = output_layer( - TensorDef(shape=[BSIZE, SEQLEN, FEEDFORWARD], dtype=dtype) - ) - - # 渲染结果 - output_proj | DefaultFormatter.with_subs(auto_symbol()) - - with block("ffn_SwiGLU_mul", 2): - st.markdown("##### SwiGLU Element-wise Mul: silu(gate) * up") - mul_layer = MulDef(dtype) - mul_out = mul_layer( - TensorDef([BSIZE, SEQLEN, FEEDFORWARD], dtype=dtype), # silu(gate) - TensorDef([BSIZE, SEQLEN, FEEDFORWARD], dtype=dtype), # up - ) - mul_out | DefaultFormatter.with_subs(auto_symbol()) - - with block("ffn_SwiGLU", 2): - st.markdown("##### SiLU Activation on Gate") - silu_layer = SiLUDef(dtype) - silu_out = silu_layer(TensorDef([BSIZE, SEQLEN, FEEDFORWARD], dtype=dtype)) - silu_out | DefaultFormatter.with_subs(auto_symbol()) - - r = row(2) - - with r.container(), block("ffn_input_block", 2): - st.markdown("##### Up Projection [Linear]") - input_layer = ColumnParallelLinear( - dtype, HIDDEN, FEEDFORWARD, False, TPSIZE, tensor_par_comm_type - ) - input_proj = input_layer( - TensorDef(shape=[BSIZE, SEQLEN, HIDDEN], dtype=dtype) - ) - input_proj | DefaultFormatter.with_subs(auto_symbol()) - - with r.container(), block("ffn_gate_block", 2): - st.markdown("##### Gate Projection [Linear]") - gate_layer = ColumnParallelLinear( - dtype, HIDDEN, FEEDFORWARD, False, TPSIZE, tensor_par_comm_type - ) - gate_proj = gate_layer( - TensorDef(shape=[BSIZE, SEQLEN, HIDDEN], dtype=dtype) - ) - gate_proj | DefaultFormatter.with_subs(auto_symbol()) - - with block("ffn_rms_block", 2): - st.markdown("##### Pre-Norm [RMSNorm]") - rms_layer = pick( - sequence_par, - SequenceParallelRMSNorm( - dtype, HIDDEN, tensor_model_parallel_size=TPSIZE - ), - RMSNormDef(dtype, HIDDEN), - ) - rms_out = rms_layer(TensorDef([BSIZE, SEQLEN, HIDDEN], dtype=dtype)) - rms_out | DefaultFormatter.with_subs(auto_symbol()) diff --git a/src/blueprinting/st/blocks/formatter.py b/src/blueprinting/st/blocks/formatter.py deleted file mode 100755 index 705aade..0000000 --- a/src/blueprinting/st/blocks/formatter.py +++ /dev/null @@ -1,186 +0,0 @@ -import functools -import html -import sys - -import hyperparameter as hp -import streamlit as st -from htbuilder import span, styles -from htbuilder.units import unit -from sympy import Expr, Symbol, latex - -from blueprinting.nn.base import TensorDef -from blueprinting.ui import NullFormatter, ReadableFLOPs, ReadableMem, ReadableTime - -PALETTE = [ - "#ff4b4b", - "#ffa421", - "#ffe312", - "#21c354", - "#00d4b1", - "#00c0f2", - "#1c83e1", - "#803df5", - "#808495", -] - -OPACITIES = [ - "33", - "66", -] - -LABEL_SPACING = unit.rem(0.5) -LABEL_FONT_SIZE = unit.rem(0.75) -LABEL_OPACITY = 0.5 -LABEL_SPACING = unit.rem(0.5) -PADDING = (unit.rem(0.25), unit.rem(0.5)) -BORDER_RADIUS = unit.rem(0.5) - -LABEL_DESC = { - "wgt": "weight mem", - "act": "activity mem", - "grad": "grad mem", - "hbm": "hbm throughput", - "c2c": "card to card comminications", -} - - -def _render_html(element, help=None): - return st.markdown(str(span(element)), unsafe_allow_html=True, help=help) - - -def labeled_text(label, body, tooltip="-", background=None, color=None, **style): - long_label = LABEL_DESC.get(label, label) - color_style = {} - - if color: - color_style["color"] = color - - if background: - background_color = background - else: - label_sum = sum(ord(c) for c in label) - background_color = PALETTE[label_sum % len(PALETTE)] - background_opacity = OPACITIES[label_sum % len(OPACITIES)] - background = background_color + background_opacity - - separator = ( - span( - style=styles( - border_bottom="1px solid", - opacity=0.1, - margin_bottom=LABEL_SPACING, - align_self="stretch", - ) - ), - ) - - label_element = ( - span( - style=styles( - margin_bottom=LABEL_SPACING, - font_size=LABEL_FONT_SIZE, - opacity=LABEL_OPACITY, - ), - title=long_label if long_label else None, - )( - html.escape(label), - ), - separator, - ) - return _render_html( - span( - style=styles( - display="inline-flex", - flex_direction="column", - align_items="center", - background=background, - border_radius=BORDER_RADIUS, - padding=PADDING, - overflow="hidden", - line_height=0.75, - **color_style, - **style, - ) - )( - label_element, - html.escape(body), - ), - help=tooltip, - ) - - -def auto_symbol(): - frame = sys._getframe(0).f_back - subs = {} - for k, v in frame.f_locals.items(): - if isinstance(v, Symbol): - subs[v.name] = frame.f_locals[k.lower()] - return subs - - -def AnnotatedFormatter(li: TensorDef, subs=None): - if subs is None: - subs = {} - human_readable = hp.scope.blueprinting.formatter.human_readable | True - mem_fmt = ReadableMem if human_readable else NullFormatter - flops_fmt = ReadableFLOPs if human_readable else NullFormatter - time_fmt = ReadableTime if human_readable else NullFormatter - - def format(fmt, val, subs): - if isinstance(val, (Symbol, Expr)): - expr = latex(val, mul_symbol=" \\times ") - value = val.subs(subs) - if value.is_number: - value = float(value) - return f"{fmt%value}", f"${expr}$" - return (f"{fmt%val}",) - - fw, bw, shapes, timming = st.tabs(["forward", "backward", "shapes", "timming"]) - with fw: - columns = [ - (labeled_text, "wgt", *format(mem_fmt, li.nbytes_weight, subs)), - (labeled_text, "act", *format(mem_fmt, li.nbytes_activity, subs)), - (labeled_text, "flops", *format(flops_fmt, li.flops_fw, subs)), - (labeled_text, "hbm", *format(mem_fmt, li.memory_fw, subs)), - # (labeled_text, "c2c", *format(flops_fmt, li.c2c_fw, subs)), - ] - cs = st.columns(len(columns)) - for col, args in zip(cs, columns): - with col: - args[0](*args[1:]) - with bw: - columns = [ - (labeled_text, "wgt", *format(mem_fmt, li.nbytes_weight, subs)), - (labeled_text, "act", *format(mem_fmt, li.nbytes_activity, subs)), - (labeled_text, "grad", *format(mem_fmt, li.nbytes_weight_grads, subs)), - (labeled_text, "flops", *format(flops_fmt, li.flops_bw, subs)), - (labeled_text, "hbm", *format(mem_fmt, li.memory_bw, subs)), - # (labeled_text, "c2c", *format(flops_fmt, li.c2c_bw, subs)), - ] - cs = st.columns(len(columns)) - for col, args in zip(cs, columns): - with col: - args[0](*args[1:]) - - with shapes: - ins = tuple(x.subs(subs) for x in li.inputs) - out = li.subs(subs) - st.markdown(f"${ins} \\rightarrow {out}$") - - with timming, hp.scope(**{"blueprinting.symbolic.subs": subs.items()}): - columns = [ - (labeled_text, "fw", *format(time_fmt, li.time_fw, subs)), - (labeled_text, "bw", *format(time_fmt, li.time_bw, subs)), - # (labeled_text, "c2c", *format(flops_fmt, "#TODO", subs)), - ] - cs = st.columns(len(columns)) - for col, args in zip(cs, columns): - with col: - args[0](*args[1:]) - - -AnnotatedFormatter.with_subs = lambda subs: functools.partial( - AnnotatedFormatter, subs=subs -) - -DefaultFormatter = AnnotatedFormatter diff --git a/src/blueprinting/types/__init__.py b/src/blueprinting/types/__init__.py deleted file mode 100755 index 6d757cb..0000000 --- a/src/blueprinting/types/__init__.py +++ /dev/null @@ -1,35 +0,0 @@ -"""Types module for blueprinting.""" - -from .counters import CommCounter -from .dtypes import DType, bf16, bfloat16, float16, float32, fp8, fp16, fp32, fp64 -from .execution import Execution -from .model import Model, ModelComm, ModelFlops, ModelParams -from .operation import Calculation, Operation -from .system import Memory, Network, Processor, System -from .tensor import TensorDef, TensorLike - -__all__ = [ - "Execution", - "Model", - "ModelParams", - "ModelFlops", - "ModelComm", - "CommCounter", - "DType", - "fp8", - "fp16", - "bf16", - "fp32", - "fp64", - "float16", - "bfloat16", - "float32", - "TensorDef", - "TensorLike", - "Operation", - "Calculation", - "System", - "Memory", - "Processor", - "Network", -] diff --git a/src/blueprinting/types/base.py b/src/blueprinting/types/base.py deleted file mode 100755 index edb7d6f..0000000 --- a/src/blueprinting/types/base.py +++ /dev/null @@ -1,29 +0,0 @@ -"""Base types for blueprinting. - -This module re-exports types from the new split modules for backward compatibility. -""" - -# Re-export from dtypes -from .dtypes import DType, bf16, bfloat16, float16, float32, fp8, fp16, fp32, fp64 - -# Re-export from operation -from .operation import Calculation, Operation - -# Re-export from tensor -from .tensor import TensorDef, TensorLike - -__all__ = [ - "DType", - "fp8", - "fp16", - "bf16", - "fp32", - "fp64", - "float16", - "bfloat16", - "float32", - "TensorDef", - "TensorLike", - "Operation", - "Calculation", -] diff --git a/src/blueprinting/types/counters.py b/src/blueprinting/types/counters.py deleted file mode 100755 index 769d8a5..0000000 --- a/src/blueprinting/types/counters.py +++ /dev/null @@ -1,75 +0,0 @@ -import dataclasses - - -@dataclasses.dataclass -class CommCounter: - """Counter for communicate operations - - Examples - -------- - >>> cnt = CommCounter() - >>> cnt.n_all_reduce - 0 - - >>> cnt.n_all_gather += 1 - >>> cnt.n_all_gather - 1 - """ - - n_all_reduce: int = 0 - n_all_gather: int = 0 - n_reduce_scatter: int = 0 - n_send_recv: int = 0 - - all_reduce: int = 0 - all_gather: int = 0 - reduce_scatter: int = 0 - send_recv: int = 0 - - def to_dict(self): - return {k: v for k, v in dataclasses.asdict(self).items() if v != 0} - - def __str__(self): - nops = " / ".join( - str(getattr(self, f.name)) - for f in dataclasses.fields(CommCounter) - if f.name.startswith("n_") - ) - ncomm = " / ".join( - str(getattr(self, f.name)) - for f in dataclasses.fields(CommCounter) - if not f.name.startswith("n_") - ) - return f"{nops}\n{ncomm}" - - def __add__(self, other): - """ - Examples - -------- - >>> cnt1 = CommCounter(n_all_reduce=1, n_all_gather=2) - >>> cnt2 = CommCounter(n_all_reduce=3, n_all_gather=4) - >>> cnt1 + cnt2 - CommCounter(n_all_reduce=4, n_all_gather=6) - - >>> import pandas as pd - >>> pd.DataFrame([cnt1, cnt2]) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE - n_all_reduce n_all_gather - 0 1 2 - 1 3 4 - >>> pd.DataFrame({"a": [cnt1, cnt2]})["a"].sum() - CommCounter(n_all_reduce=4, n_all_gather=6) - """ - cnt = CommCounter() - for f in dataclasses.fields(CommCounter): - name = f.name - setattr(cnt, name, getattr(self, name) + getattr(other, name)) - return cnt - - def __mul__(self, scale): - cnt = CommCounter() - for f in dataclasses.fields(CommCounter): - name = f.name - setattr(cnt, name, getattr(self, name) * scale) - return cnt - - __rmul__ = __mul__ diff --git a/src/blueprinting/types/dtypes.py b/src/blueprinting/types/dtypes.py deleted file mode 100644 index a8dec4b..0000000 --- a/src/blueprinting/types/dtypes.py +++ /dev/null @@ -1,70 +0,0 @@ -"""Data types for blueprinting.""" - -from dataclasses import dataclass -from enum import Enum - -__all__ = [ - "DType", - "FloatPoint", - "fp8", - "fp16", - "bf16", - "fp32", - "fp64", - "float16", - "bfloat16", - "float32", -] - - -@dataclass -class FloatPoint: - kind: str - size: int - - -class DType(FloatPoint, Enum): - """data types - - Examples - -------- - >>> DType.fp8 - fp8 - >>> DType.fp16 == DType.float16 - True - >>> DType("fp16") - fp16 - >>> DType("float16") - fp16 - >>> list(DType) - [fp8, fp16, bf16, fp32, fp64] - """ - - fp8 = "fp8", 1 - fp16 = "fp16", 2 - bf16 = "bf16", 2 - fp32 = "fp32", 4 - fp64 = "fp64", 8 - float16 = "fp16", 2 - bfloat16 = "bf16", 2 - float32 = "fp32", 4 - - def __repr__(self): - return self.name - - @classmethod - def _missing_(cls, value): - for x in cls: - if x.name == value: - return x - if value == "float16": - return cls.fp16 - if value == "bfloat16": - return cls.bf16 - if value == "float32": - return cls.fp32 - return None - - -fp8, fp16, bf16, fp32, fp64 = list(DType) -float16, bfloat16, float32 = DType.float16, DType.bfloat16, DType.float32 diff --git a/src/blueprinting/types/execution.py b/src/blueprinting/types/execution.py deleted file mode 100755 index 1915985..0000000 --- a/src/blueprinting/types/execution.py +++ /dev/null @@ -1,47 +0,0 @@ -class Execution: - def __init__(self, cfg) -> None: - self.num_procs = cfg.num_procs | 0 - self.tensor_par = cfg.tensor_par | 0 - self.pipeline_par = cfg.pipeline_par | 0 - self.data_par = cfg.data_par | 0 - self.tensor_par_net = cfg.tensor_par_net | 0 - self.pipeline_par_net = cfg.pipeline_par_net | 0 - self.data_par_net = cfg.data_par_net | 0 - self.global_batch_size = cfg.batch_size | 0 - - self.microbatch_size = cfg.microbatch_size | 0 - - self.datatype = cfg.datatype | 0 - self.fused_activation = cfg.fused_activation | 0 - self.attention_type = ( - cfg.attention_type | "multihead" - ) # ['multihead', 'multiquery'] - self.activation_recompute = ( - cfg.activation_recompute | "none" - ) # ['full', 'attn_only', 'none'] - - self.pipeline_interleaving = cfg.pipeline_interleaving | 0 - self.optimizer_sharding = cfg.optimizer_sharding | 0 - self.tensor_par_comm_type = ( - cfg.tensor_par_comm_type | "ar" - ) # ['ar', 'p2p_rs_ag', 'rs_ag'] - - self.tensor_par_overlap = ( - cfg.tensor_par_overlap | "ring" - ) # ['none', 'ring', 'pipe'] - self.seq_par_ag_redo = cfg.seq_par_ag_redo | 0 - self.data_par_overlap = cfg.data_par_overlap | 0 - - self.weight_offload = cfg.weight_offload | 0 - self.activations_offload = cfg.activations_offload | 0 - self.optimizer_offload = cfg.optimizer_offload | 0 - self.training = cfg.training | 0 - - self._local_batch_size = self.global_batch_size // self.data_par - self._sequence_par = self.tensor_par_comm_type == "rs_ag" - self._pipeline_par_rs_ag = self.tensor_par_comm_type in ["p2p_rs_ag", "rs_ag"] - self.in_network_reduction = False - self._num_microbatches = self._local_batch_size // self.microbatch_size - - def get_json(self): - return self.__dict__ diff --git a/src/blueprinting/types/model/__init__.py b/src/blueprinting/types/model/__init__.py deleted file mode 100644 index 3ebd133..0000000 --- a/src/blueprinting/types/model/__init__.py +++ /dev/null @@ -1,172 +0,0 @@ -"""Model module for blueprinting.""" - -import hyperparameter as hp - -from .comm import ModelComm -from .flops import ModelFlops -from .params import ModelParams - -__all__ = ["Model", "ModelParams", "ModelFlops", "ModelComm"] - - -class Model: - """LLM Model configuration. - - Uses composition pattern instead of dynamic inheritance. - - Examples - -------- - >>> import hyperparameter as hp - >>> with hp.scope(hidden=512, feedforward=2048, attn_heads=8, attn_size=64, seq_size=1024, num_blocks=4): - ... m = Model() - ... m.hidden - 512 - >>> m.nparam_total - 39348224 - """ - - @staticmethod - def from_cfg(cfg=None) -> "Model": - """Create Model from configuration. - - Args: - cfg: hp.scope object or None to use current scope - - Returns: - Model instance - """ - return Model(cfg) - - def __init__(self, cfg=None) -> None: - # Read configuration from hp.scope at runtime - if cfg is None: - cfg = hp.scope() - self.hidden = cfg.hidden | 0 - self.feedforward = cfg.feedforward | 0 - self.seq_size = cfg.seq_size | 0 - self.attn_heads = cfg.attn_heads | 0 - self.attn_size = cfg.attn_size | 0 - self.num_blocks = cfg.num_blocks | 0 - - # Composition pattern - delegate to specialized classes - self._params = ModelParams(self) - self._flops = ModelFlops(self) - self._comm = ModelComm(self) - - def num_parameters(self) -> int: - """Calculate total number of parameters. - - https://cs.stanford.edu/~matei/papers/2021/sc_megatron_lm.pdf - Equation 2 - """ - p = 2 * self.hidden * self.feedforward # MLP weights - p += 4 * self.hidden * self.attn_heads * self.attn_size # Attn weights - p += self.hidden + self.feedforward # biases MLP - p += 3 * self.attn_heads * self.attn_size + self.hidden # biases Attn - p += 2 * 2 * self.hidden # layer norm - p *= self.num_blocks # per each block - p += (51200 + self.seq_size) * self.hidden # embeddings - return p - - # Delegate parameter calculations to ModelParams - @property - def nparam_mlp(self) -> int: - """MLP parameters.""" - return self._params.mlp() - - @property - def nparam_attn(self) -> int: - """Attention parameters.""" - return self._params.attn() - - @property - def nparam_norm(self) -> int: - """Normalization layer parameters.""" - return self._params.norm() - - @property - def nparam_embedding(self) -> int: - """Embedding parameters.""" - return self._params.embedding() - - @property - def nparam_total(self) -> int: - """Total number of parameters in the model.""" - return self._params.total() - - # Delegate FLOPs calculations to ModelFlops - @property - def flops_mlp(self) -> int: - """MLP FLOPs.""" - return self._flops.mlp() - - @property - def flops_attn(self) -> int: - """Attention FLOPs.""" - return self._flops.attn() - - @property - def flops_norm(self) -> int: - """Normalization layer FLOPs.""" - return self._flops.norm() - - @property - def flops_embedding(self) -> int: - """Embedding FLOPs.""" - return self._flops.embedding() - - @property - def flops_total(self) -> int: - """Total FLOPs.""" - return self._flops.total() - - # Delegate communication calculations to ModelComm - @property - def comm_embedding_fw(self): - """Forward embedding communication.""" - return self._comm.embedding_fw() - - @property - def comm_embedding_bw(self): - """Backward embedding communication.""" - return self._comm.embedding_bw() - - @property - def comm_attn_fw(self): - """Forward attention communication.""" - return self._comm.attn_fw() - - @property - def comm_attn_bw(self): - """Backward attention communication.""" - return self._comm.attn_bw() - - @property - def comm_mlp_fw(self): - """Forward MLP communication.""" - return self._comm.mlp_fw() - - @property - def comm_mlp_bw(self): - """Backward MLP communication.""" - return self._comm.mlp_bw() - - @property - def comm_total_fw(self): - """Total forward communication.""" - return self._comm.total_fw() - - @property - def comm_total_bw(self): - """Total backward communication.""" - return self._comm.total_bw() - - @property - def comm_pipeline_parallel_fw(self): - """Forward pipeline parallel communication.""" - return self._comm.pipeline_parallel_fw() - - @property - def comm_pipeline_parallel_bw(self): - """Backward pipeline parallel communication.""" - return self._comm.pipeline_parallel_bw() diff --git a/src/blueprinting/types/model/comm.py b/src/blueprinting/types/model/comm.py deleted file mode 100644 index 2155388..0000000 --- a/src/blueprinting/types/model/comm.py +++ /dev/null @@ -1,184 +0,0 @@ -"""Model communication calculations for blueprinting.""" - -import hyperparameter as hp - -from ..counters import CommCounter - -__all__ = ["ModelComm"] - - -class ModelComm: - """Model communication calculations.""" - - def __init__(self, model: "Model"): - self._model = model - - @hp.param("exe") - def embedding_fw( - self, global_batch_size=0, microbatch_size=0, data_par=0, tensor_par=0 - ) -> CommCounter: - """Forward embedding communication.""" - m = self._model - cnt = CommCounter() - if tensor_par > 0: - cnt.n_all_reduce += 1 - cnt.all_reduce += microbatch_size * m.seq_size * m.hidden - - cnt.n_all_reduce += 1 - cnt.all_reduce += microbatch_size * m.seq_size - - cnt.n_all_reduce += 1 - cnt.all_reduce += tensor_par - return cnt - - @hp.param("exe") - def embedding_bw( - self, global_batch_size=0, microbatch_size=0, data_par=0, tensor_par=0 - ) -> CommCounter: - """Backward embedding communication.""" - return CommCounter() - - @hp.param("exe") - def attn_fw( - self, - global_batch_size=0, - microbatch_size=0, - data_par=0, - tensor_par=0, - sequence_par=True, - ) -> CommCounter: - """Forward attention communication.""" - m = self._model - cnt = CommCounter() - if tensor_par > 0 and not sequence_par: - cnt.n_all_reduce += 1 - cnt.all_reduce += microbatch_size * m.seq_size * m.hidden - elif tensor_par > 0 and sequence_par: - cnt.n_all_gather += 1 - cnt.all_gather += microbatch_size * m.seq_size * m.hidden - - cnt.n_reduce_scatter += 1 - cnt.reduce_scatter += microbatch_size * m.seq_size * m.hidden - return cnt - - @hp.param("exe") - def attn_bw( - self, - global_batch_size=0, - microbatch_size=0, - data_par=0, - tensor_par=0, - sequence_par=True, - ) -> CommCounter: - """Backward attention communication.""" - m = self._model - cnt = CommCounter() - if tensor_par > 0 and not sequence_par: - cnt.n_all_reduce += 1 - cnt.all_reduce += microbatch_size * m.seq_size * m.hidden - elif tensor_par > 0 and sequence_par: - cnt.n_all_gather += 1 - cnt.all_gather += microbatch_size * m.seq_size * m.hidden - - cnt.n_reduce_scatter += 1 - cnt.reduce_scatter += microbatch_size * m.seq_size * m.hidden - return cnt - - @hp.param("exe") - def mlp_fw( - self, - microbatch_size=0, - zero_optimizer=0, - data_par=0, - tensor_par=0, - sequence_par=True, - ) -> CommCounter: - """Forward MLP communication.""" - m = self._model - cnt = CommCounter() - if tensor_par > 0 and not sequence_par: - # forward: attn -> all_reduce -> mlp - cnt.n_all_reduce += 1 - cnt.all_reduce += microbatch_size * m.seq_size * m.hidden - elif tensor_par > 0 and sequence_par: - cnt.n_all_gather += 1 - cnt.all_gather += microbatch_size * m.seq_size * m.hidden - - cnt.n_reduce_scatter += 1 - cnt.reduce_scatter += microbatch_size * m.seq_size * m.hidden - - if data_par > 0 and zero_optimizer == 3: - cnt.n_all_gather += 1 - cnt.all_gather += m.hidden * m.hidden - return cnt - - @hp.param("exe") - def mlp_bw( - self, - microbatch_size=0, - zero_optimizer=0, - data_par=0, - tensor_par=0, - sequence_par=True, - ) -> CommCounter: - """Backward MLP communication.""" - m = self._model - cnt = CommCounter() - if tensor_par > 0 and not sequence_par: - # backward: attn <- all_reduce <- mlp - cnt.n_all_reduce += 1 - cnt.all_reduce += microbatch_size * m.seq_size * m.hidden - elif tensor_par > 0 and sequence_par: - cnt.n_all_gather += 1 - cnt.all_gather += microbatch_size * m.seq_size * m.hidden - - cnt.n_reduce_scatter += 1 - cnt.reduce_scatter += microbatch_size * m.seq_size * m.hidden - - if data_par > 0 and zero_optimizer == 0: - # 梯度累加，梯度下发 - cnt.n_all_reduce += 1 - cnt.all_reduce += m.hidden * m.hidden - elif data_par > 0 and zero_optimizer == 1: - # 梯度累加，梯度下发 - cnt.n_all_reduce += 1 - cnt.all_reduce += m.hidden * m.hidden - - # 参数聚合 - cnt.n_all_gather += 1 - cnt.all_gather += m.hidden * m.hidden - elif data_par > 0 and zero_optimizer == 2: - # 梯度聚合 - cnt.n_reduce_scatter += 1 - cnt.reduce_scatter += m.hidden * m.hidden - - # 参数聚合 - cnt.n_all_gather += 1 - cnt.all_gather += m.hidden * m.hidden - elif data_par > 0 and zero_optimizer == 3: - # 参数聚合 - cnt.n_all_gather += 1 - cnt.all_gather += m.hidden * m.hidden - - # 梯度聚合 - cnt.n_reduce_scatter += 1 - cnt.reduce_scatter += m.hidden * m.hidden - return cnt - - @hp.param("exe") - def pipeline_parallel_fw(self, microbatch_size=0, pipeline_par=0) -> CommCounter: - """Forward pipeline parallel communication.""" - return CommCounter() - - @hp.param("exe") - def pipeline_parallel_bw(self, microbatch_size=0, pipeline_par=0) -> CommCounter: - """Backward pipeline parallel communication.""" - return CommCounter() - - def total_fw(self) -> CommCounter: - """Total forward communication.""" - return self.embedding_fw() + self.attn_fw() + self.mlp_fw() - - def total_bw(self) -> CommCounter: - """Total backward communication.""" - return self.embedding_bw() + self.attn_bw() + self.mlp_bw() diff --git a/src/blueprinting/types/model/flops.py b/src/blueprinting/types/model/flops.py deleted file mode 100644 index 34f23cd..0000000 --- a/src/blueprinting/types/model/flops.py +++ /dev/null @@ -1,53 +0,0 @@ -"""Model FLOPs calculations for blueprinting.""" - -import hyperparameter as hp - -__all__ = ["ModelFlops"] - - -class ModelFlops: - """Model FLOPs calculations.""" - - def __init__(self, model: "Model"): - self._model = model - - @hp.param("model") - def mlp(self) -> int: - """MLP FLOPs.""" - return 2 * self._model._params.mlp() - - @hp.param("model") - def attn(self, use_qkv_bias=True, use_attn_bias=True) -> int: - """Attention FLOPs.""" - m = self._model - qkv_proj_input = m.hidden - qkv_proj_output = m.attn_size - - attn_proj_input = m.attn_size * m.attn_heads - attn_proj_output = m.hidden - - qkv_flops = 2 * 3 * m.attn_heads * qkv_proj_input * qkv_proj_output - proj_flops = 2 * attn_proj_input * attn_proj_output - mask_flops = 2 * m.seq_size * m.attn_heads * m.attn_size - - return qkv_flops + proj_flops + mask_flops - - @hp.param("model") - def norm(self, type_norm="LN") -> int: - """Normalization layer FLOPs.""" - m = self._model - if type_norm == "LN": - return 2 * m.hidden - if type_norm == "RMS": - return m.hidden - return 0 - - @hp.param("model") - def embedding(self) -> int: - """Embedding FLOPs.""" - return 0 - - @hp.param("model") - def total(self) -> int: - """Total FLOPs.""" - return 0 diff --git a/src/blueprinting/types/model/params.py b/src/blueprinting/types/model/params.py deleted file mode 100644 index 39ae7a3..0000000 --- a/src/blueprinting/types/model/params.py +++ /dev/null @@ -1,93 +0,0 @@ -"""Model parameter calculations for blueprinting.""" - -import hyperparameter as hp - -__all__ = ["ModelParams"] - - -class ModelParams: - """Model parameter calculations. - - Examples - -------- - >>> from blueprinting.types.model import Model - >>> import hyperparameter as hp - >>> with hp.scope(hidden=512, feedforward=2048, attn_heads=8, attn_size=64, seq_size=1024, num_blocks=4): - ... m = Model() - ... m.nparam_mlp - 2099712 - """ - - def __init__(self, model: "Model"): - self._model = model - - @hp.param("model") - def mlp(self, use_mlp_bias=True) -> int: - """MLP parameters. - - Parameters - ---------- - use_mlp_bias : bool, optional - Whether to include bias parameters, by default True - """ - m = self._model - bias = m.hidden + m.feedforward if use_mlp_bias else 0 - return 2 * m.feedforward * m.hidden + bias - - @hp.param("model") - def attn(self, use_qkv_bias=True, use_attn_bias=True) -> int: - """Attention parameters. - - Parameters - ---------- - use_qkv_bias : bool, optional - Whether to include bias in QKV projection, by default True - use_attn_bias : bool, optional - Whether to include bias in attention projection, by default True - """ - m = self._model - qkv_proj_input = m.hidden - qkv_proj_output = m.attn_size - qkv_proj_bias = m.attn_size if use_qkv_bias else 0 - - attn_proj_input = m.attn_size * m.attn_heads - attn_proj_output = m.hidden - attn_proj_bias = m.hidden if use_attn_bias else 0 - - return 3 * m.attn_heads * (qkv_proj_input * qkv_proj_output + qkv_proj_bias) + ( - attn_proj_input * attn_proj_output + attn_proj_bias - ) - - @hp.param("model") - def norm(self, type_norm="LN") -> int: - """Normalization layer parameters.""" - m = self._model - if type_norm == "LN": - input_norm = 2 * m.hidden - attn_norm = 2 * m.hidden - elif type_norm == "RMS": - input_norm = 0 - attn_norm = 0 - else: - input_norm = 0 - attn_norm = 0 - return input_norm + attn_norm - - @hp.param("model") - def embedding(self, vocab_size=51200, type_posemb="learned") -> int: - """Embedding parameters.""" - m = self._model - if type_posemb == "learned": - posemb = m.seq_size * m.hidden - elif type_posemb == "rope": - posemb = 0 - else: - posemb = 0 - return vocab_size * m.hidden + posemb - - def total(self) -> int: - """Total number of parameters in the model.""" - m = self._model - return ( - m.num_blocks * (self.mlp() + self.attn() + self.norm()) + self.embedding() - ) diff --git a/src/blueprinting/types/operation.py b/src/blueprinting/types/operation.py deleted file mode 100644 index 1f8a809..0000000 --- a/src/blueprinting/types/operation.py +++ /dev/null @@ -1,130 +0,0 @@ -"""Operation and Calculation definitions for blueprinting.""" - -from dataclasses import dataclass, field -from typing import List, Optional, Tuple - -import hyperparameter as hp - -from .tensor import TensorDef - -__all__ = [ - "Operation", - "Calculation", -] - - -@dataclass -class Operation: - """Base operation class.""" - - inputs: Tuple[TensorDef, ...] = () - output: TensorDef = field(default_factory=lambda: TensorDef()) - - @property - def shape(self): - return self.output.shape - - @property - def dtype(self): - return self.output.dtype - - @property - def nelems(self): - return self.output.nelems - - @property - def nbytes(self): - return self.output.nbytes - - @property - def dsize(self): - return self.output.dsize - - -@dataclass -class Calculation(Operation): - """Calculation node in computation graph.""" - - function: Optional["LayerDef"] = None - - @property - def nelems(self) -> int: - return self.output.nelems - - @property - def nbytes(self): - return self.output.nelems * self.output.dsize - - @property - def nbytes_weight(self) -> int: - return self.function.nbytes_weight - - @property - def nbytes_weight_grads(self) -> int: - return self.function.nbytes_weight_grads - - @property - def nbytes_activity(self) -> int: - with hp.scope(**{"blueprinting.layerdef.inputs": self.inputs}): - return self.function.nbytes_activity - - @property - def flops_fw(self) -> int: - with hp.scope(**{"blueprinting.layerdef.inputs": self.inputs}): - return self.function.flops_fw - - @property - def flops_bw(self) -> int: - with hp.scope(**{"blueprinting.layerdef.inputs": self.inputs}): - return self.function.flops_bw - - @property - def memrw_fw(self) -> int: - with hp.scope(**{"blueprinting.layerdef.inputs": self.inputs}): - return self.function.memrw_fw - - @property - def memrw_bw(self) -> int: - with hp.scope(**{"blueprinting.layerdef.inputs": self.inputs}): - return self.function.memrw_bw - - @property - def time_fw(self) -> int: - with hp.scope(**{"blueprinting.layerdef.inputs": self.inputs}): - return self.function.time_fw - - @property - def time_bw(self) -> int: - with hp.scope(**{"blueprinting.layerdef.inputs": self.inputs}): - return self.function.time_bw - - @property - def memory_fw(self) -> int: - with hp.scope(**{"blueprinting.layerdef.inputs": self.inputs}): - return self.function.memory_fw - - @property - def memory_bw(self) -> int: - with hp.scope(**{"blueprinting.layerdef.inputs": self.inputs}): - return self.function.memory_bw - - @property - @hp.param("blueprinting.layerdef") - def placement_weight(self, inputs: List[TensorDef] = None) -> Tuple[TensorDef, ...]: - if inputs is None: - inputs = [] - with hp.scope(**{"blueprinting.layerdef.inputs": self.inputs}): - return self.function.placement_weight - - def subs(self, subs=None): - """Substitute symbolic values in the output tensor.""" - if subs is None: - subs = {} - return self.output.subs(subs) - - def belike(self): - """Return a TensorDef with the same shape and dtype as output.""" - return self.output.belike() - - def __or__(self, other): - other(self) diff --git a/src/blueprinting/types/system/__init__.py b/src/blueprinting/types/system/__init__.py deleted file mode 100755 index e278426..0000000 --- a/src/blueprinting/types/system/__init__.py +++ /dev/null @@ -1,102 +0,0 @@ -"""System module for blueprinting.""" - -from typing import List - -import hyperparameter as hp - -from .memory import Memory -from .network import Network -from .processor import Processor - -__all__ = ["System", "Memory", "Processor", "Network"] - - -class System: - """Hardware system configuration. - - Examples - -------- - >>> import json - >>> import hyperparameter as hp - >>> sys_cfg = json.load(open("system.json")) - >>> with hp.scope(sys=sys_cfg): - ... system = System() - ... system.proc_mode - 'roofline' - """ - - TypeSizes = { - "float8": 1, - "float16": 2, - "float32": 4, - "bfloat16": 2, - } - - @staticmethod - def supported_datatypes() -> List[str]: - """Return list of supported data types.""" - return list(System.TypeSizes.keys()) - - def __init__(self, cfg=None) -> None: - if cfg is None: - cfg = hp.scope() - - self.matrix = Processor("matrix") - self.vector = Processor("vector") - self.datatype = None - - self.mem1 = Memory("mem1") - self.mem2 = Memory("mem2") - - self.proc_mode = cfg.processing_mode | "roofline" - assert self.proc_mode in ["roofline", "no_overlap"] - - # Networks is a list of dicts, create each with its own scope - networks_cfg = cfg.networks | [] - self.networks = [] - for net_dict in networks_cfg: - with hp.scope(**net_dict): - self.networks.append(Network()) - - @property - def num_networks(self) -> int: - """Number of networks.""" - return len(self.networks) - - def get_network(self, tier: int) -> Network: - """Get network by tier.""" - assert tier < len(self.networks), f"Bad network tier ID: {tier}" - return self.networks[tier] - - def set_datatype(self, datatype: str) -> None: - """Set the data type for processing.""" - assert datatype in System.TypeSizes, f"Unsupported data type: {datatype}" - self.datatype = datatype - - def get_matrix_throughput(self, flops: int) -> float: - """Get matrix processor throughput.""" - return self.matrix.throughput(self.datatype, flops) - - def get_vector_throughput(self, flops: int) -> float: - """Get vector processor throughput.""" - return self.vector.throughput(self.datatype, flops) - - def get_mem1_throughput(self, size: int) -> float: - """Get primary memory throughput.""" - return self.mem1.throughput(size) - - def get_mem2_throughput(self, size: int) -> float: - """Get secondary memory throughput.""" - return self.mem2.throughput(size) - - def compute_offload_time(self, size: int) -> float: - """Compute time to offload data to secondary memory.""" - return size / self.mem2.throughput(size) - - def get_processing_time(self, flops_time: float, mem_time: float) -> float: - """Get processing time based on processing mode.""" - if self.proc_mode == "roofline": - return max(flops_time, mem_time) - elif self.proc_mode == "no_overlap": - return flops_time + mem_time - return flops_time + mem_time diff --git a/src/blueprinting/types/system/memory.py b/src/blueprinting/types/system/memory.py deleted file mode 100755 index 803f8c5..0000000 --- a/src/blueprinting/types/system/memory.py +++ /dev/null @@ -1,45 +0,0 @@ -"""Memory configuration for blueprinting.""" - -import hyperparameter as hp - -__all__ = ["Memory"] - - -class Memory: - """Memory configuration. - - Examples - -------- - >>> import hyperparameter as hp - >>> with hp.scope(**{"mem1.GiB": 80, "mem1.GBps": 2000, "mem1.MB_efficiency": [(100, 0.9)]}): - ... mem = Memory("mem1") - ... mem.capacity - 85899345920 - """ - - def __init__(self, prefix: str) -> None: - """Initialize Memory. - - Args: - prefix: The configuration prefix (e.g., "mem1", "mem2") - """ - cfg = getattr(hp.scope(), prefix) - - self.capacity = (cfg.GiB | 0) * 1024**3 - self.bandwidth = (cfg.GBps | 0) * 1e9 - self._efficiency = [] - for mbytes, eff in cfg.MB_efficiency | []: - bytes = mbytes * 1e6 - assert 0 < eff <= 1.0 - self._efficiency.append((bytes, eff)) - - def efficiency(self, op_bytes: int) -> float: - """Get efficiency for given operation size.""" - for bytes, eff in self._efficiency: - if op_bytes >= bytes: - return eff - raise ValueError(f"OP bytes {op_bytes} wasn't covered by efficiency curve") - - def throughput(self, op_bytes: int) -> float: - """Get throughput for given operation size.""" - return self.bandwidth * self.efficiency(op_bytes) diff --git a/src/blueprinting/types/system/network.py b/src/blueprinting/types/system/network.py deleted file mode 100644 index 09f22fe..0000000 --- a/src/blueprinting/types/system/network.py +++ /dev/null @@ -1,120 +0,0 @@ -"""Network configuration for blueprinting.""" - -from dataclasses import dataclass - -import hyperparameter as hp - -__all__ = ["Network", "NetworkOp"] - - -@dataclass -class NetworkOp: - """Network operation configuration.""" - - scalar: float - offset: float - - -class Network: - """Network configuration. - - Examples - -------- - >>> import hyperparameter as hp - >>> with hp.scope(bandwidth=100, efficiency=0.9, size=8, latency=1e-6): - ... net = Network() - ... net.bandwidth - 100000000000.0 - """ - - COLLECTIVES = {"reduce_scatter", "all_gather", "all_reduce"} - NET_OPS = {"p2p", "reduce_scatter", "all_gather", "all_reduce"} - - def __init__(self) -> None: - """Initialize Network from current hp.scope.""" - cfg = hp.scope() - - self._bandwidth = (cfg.bandwidth | 0) * 1e9 # GB/s -> B/s - self._efficiency = cfg.efficiency | 1.0 - assert 0 < self._efficiency <= 1.0 - self._size = cfg.size | 0 - self._latency = cfg.latency | 0 - self._must_be_filled = cfg.must_be_filled | False - self._processor_usage = cfg.processor_usage | 0.0 - assert 0.0 <= self._processor_usage < 1.0 - - # Get ops configuration - extract op names from flat keys - self._ops = {} - all_keys = cfg.keys() - ops_keys = [k for k in all_keys if k.startswith("ops.")] - op_names = {k.split(".")[1] for k in ops_keys if len(k.split(".")) > 1} - - for op in op_names: - op_cfg = getattr(cfg.ops, op) - # op_cfg is a list [scalar, offset] - params = op_cfg | [1.0, None] - if isinstance(params, (list, tuple)): - scalar, offset = params - else: - scalar = 1.0 - offset = None - - assert op in self.NET_OPS, f"Invalid network op: {op}" - assert scalar > 0.0, f"Invalid network scalar for {op}: {scalar}" - - if op in self.COLLECTIVES: - assert offset is not None, f"Must give offset for {op}" - self._ops[op] = NetworkOp(scalar, offset) - else: - assert offset is None, f"Can't give offset for {op}" - self._ops[op] = NetworkOp(scalar, 0) - - @property - def bandwidth(self) -> float: - """Bandwidth in bytes/second.""" - return self._bandwidth - - @property - def size(self) -> int: - """Network size (number of nodes).""" - return self._size - - @property - def must_be_filled(self) -> bool: - """Whether network must be fully utilized.""" - return self._must_be_filled - - @property - def processor_usage(self) -> float: - """Processor usage during network operations.""" - return self._processor_usage - - def time(self, op: str, op_size: int, comm_size: int) -> float: - """Compute time for a network operation. - - Args: - op: Operation name (p2p, reduce_scatter, all_gather, all_reduce) - op_size: Operation size in bytes - comm_size: Number of participants in operation - - Returns: - Time needed for operation in seconds - """ - if op not in self.COLLECTIVES: - assert comm_size == 2 - else: - assert comm_size >= 2 - assert op in self.NET_OPS - assert op_size >= 0 - - net_op = self._ops[op] - - # Scale the op_size by the scalar - scaled_size = op_size * net_op.scalar - - # Scale the op_size by the op offset - chunk_size = scaled_size / comm_size - total_size = scaled_size + chunk_size * net_op.offset - - # Calculate time based on raw bandwidth, efficiency, and latency - return self._latency + total_size / (self._bandwidth * self._efficiency) diff --git a/src/blueprinting/types/system/processor.py b/src/blueprinting/types/system/processor.py deleted file mode 100644 index 8eed088..0000000 --- a/src/blueprinting/types/system/processor.py +++ /dev/null @@ -1,73 +0,0 @@ -"""Processor configuration for blueprinting.""" - -import hyperparameter as hp - -__all__ = ["Processor"] - - -class Processor: - """Processor configuration with support for multiple data types. - - Examples - -------- - >>> import hyperparameter as hp - >>> with hp.scope(**{"matrix.float16.tflops": 312, "matrix.float16.gflops_efficiency": [(100, 0.9)]}): - ... proc = Processor("matrix") - ... proc.flops("float16") - 312000000000000.0 - """ - - def __init__(self, prefix: str) -> None: - """Initialize Processor. - - Args: - prefix: The configuration prefix (e.g., "matrix", "vector") - """ - cfg = hp.scope() - self._datatypes = {} - - # Get keys that match this prefix (e.g., "matrix.float16.tflops") - all_keys = cfg.keys() - prefix_dot = f"{prefix}." - - # Extract unique data types under this prefix - # "matrix.float16.tflops" -> "float16" - dtypes = set() - for k in all_keys: - if k.startswith(prefix_dot): - parts = k[len(prefix_dot) :].split(".") - if parts: - dtypes.add(parts[0]) - - # Build configuration for each data type - for dtype in dtypes: - dt_cfg = getattr(getattr(cfg, prefix), dtype) - tflops = dt_cfg.tflops | 0 - gflops_eff = dt_cfg.gflops_efficiency | [] - - self._datatypes[dtype] = {"flops": tflops * 1e12, "efficiency": []} - last = None - for gflops, eff in gflops_eff: - flops = gflops * 1e9 - assert 0 < eff <= 1.0 - if last: - assert flops < last - last = flops - self._datatypes[dtype]["efficiency"].append((flops, eff)) - - def flops(self, datatype: str) -> float: - """Get peak flops for given data type.""" - return self._datatypes[datatype]["flops"] - - def efficiency(self, datatype: str, op_flops: int) -> float: - """Get efficiency for given data type and operation flops.""" - for flops, eff in self._datatypes[datatype]["efficiency"]: - if op_flops >= flops: - return eff - raise ValueError(f"{op_flops} wasn't covered in {datatype} efficiency curve") - - def throughput(self, datatype: str, op_flops: int) -> float: - """Get throughput for given data type and operation flops.""" - if datatype not in self._datatypes: - raise ValueError(f"Unsupported data type: {datatype}") - return self.flops(datatype) * self.efficiency(datatype, op_flops) diff --git a/src/blueprinting/types/tensor.py b/src/blueprinting/types/tensor.py deleted file mode 100644 index 19ef30a..0000000 --- a/src/blueprinting/types/tensor.py +++ /dev/null @@ -1,66 +0,0 @@ -"""Tensor definitions for blueprinting.""" - -from dataclasses import dataclass, field -from functools import reduce -from operator import mul -from typing import Tuple, Union - -from sympy import Expr - -from .dtypes import DType, fp32 - -__all__ = [ - "TensorLike", - "TensorDef", -] - - -class TensorLike: - """Tensor protocol mixin.""" - - @property - def nelems(self): - return reduce(mul, self.shape, 1) - - @property - def nbytes(self): - return self.dsize * self.nelems - - @property - def dsize(self): - return self.dtype.size - - @property - def T(self): - return TensorDef(list(reversed(self.shape)), self.dtype) - - def belike(self): - return TensorDef(list(self.shape), self.dtype) - - def __repr__(self) -> str: - return f"{self.dtype}{self.shape}" - - def subs(self, subs=None) -> "TensorDef": - if subs is None: - subs = {} - return TensorDef( - [int(x.subs(subs)) if isinstance(x, Expr) else x for x in self.shape], - dtype=self.dtype, - ) - - -@dataclass -class TensorDef(TensorLike): - """TensorDef - - Examples - -------- - >>> TensorDef([1, 2, 3], dtype=DType.fp32) - fp32[1, 2, 3] - """ - - shape: Tuple[Union[int, Expr], ...] = () - dtype: DType = field(default_factory=lambda: fp32) - - def __repr__(self) -> str: - return f"{repr(self.dtype)}{self.shape}" diff --git a/src/blueprinting/ui.py b/src/blueprinting/ui.py deleted file mode 100755 index b178f68..0000000 --- a/src/blueprinting/ui.py +++ /dev/null @@ -1,666 +0,0 @@ -"""UI utilities for blueprinting - Streamlit 共享组件和工具函数""" - -import glob -import hmac -import json -from dataclasses import dataclass -from typing import Callable, Union - -import hyperparameter as hp -import pandas as pd -import streamlit as st -from streamlit_extras.row import row as st_row - -from . import io - -# ============================================================================ -# 页面配置和初始化 -# ============================================================================ - - -def check_password(): - """Returns `True` if the user had a correct password.""" - - def login_form(): - """Form with widgets to collect user information""" - with st.form("Credentials"): - st.text_input("Username", key="username") - st.text_input("Password", type="password", key="password") - st.form_submit_button("Log in", on_click=password_entered) - - def password_entered(): - """Checks whether a password entered by the user is correct.""" - if st.session_state["username"] in st.secrets[ - "passwords" - ] and hmac.compare_digest( - st.session_state["password"], - st.secrets.passwords[st.session_state["username"]], - ): - st.session_state["password_correct"] = True - del st.session_state["password"] # Don't store the username or password. - del st.session_state["username"] - else: - st.session_state["password_correct"] = False - - # Return True if the username + password is validated. - if st.session_state.get("password_correct", False): - return True - - # Show inputs for username + password. - login_form() - if "password_correct" in st.session_state: - st.error("😕 User not known or password incorrect") - return False - - -def ele(name, *args, scope=None, param=None, **kwargs): - return (name, scope, param, args, kwargs) - - -def row(*args): - columns = st.columns([1 for _ in args], vertical_alignment="bottom") - for element, container in zip(args, columns): - with container: - value = getattr(st, element[0])(*element[3], **element[4]) - if element[1] is not None and element[2] is not None: - setattr(element[1], element[2], value) - - -class Predefined: - @property - def models(self): - return glob.glob("data/models/*.json") - - @property - def model_names(self): - return [x.replace("data/models/", "") for x in self.models] - - @property - def systems(self): - return glob.glob("data/systems/*.json") - - @property - def system_names(self): - return [x.replace("data/systems/", "") for x in self.systems] - - @property - def executions(self): - return glob.glob("data/examples/*.json") - - @property - def execution_names(self): - return [x.replace("data/examples/", "") for x in self.executions] - - -predefined = Predefined() - - -def value2widget(key, value, prefix=None): - if prefix: - key = f"{prefix}.{key}" - if isinstance(value, bool): - return st.checkbox(key, value=value) - if isinstance(value, (int, float)): - return st.number_input(key, value=value) - if isinstance(value, str): - return st.text_input(key, value=value) - if isinstance(value, dict): - return {k: value2widget(k, v, prefix=key) for k, v in value.items()} - if isinstance(value, list): - return [value2widget(str(k), v, prefix=key) for k, v in enumerate(value)] - return value - - -def make_json_conf(conf): - if isinstance(conf, str): - conf = io.read_json_file(conf) - return {k: value2widget(k, v) for k, v in conf.items()} - - -LEVELS = {"block": "块"} -STAGES = { - "fw": "前向", - "agrad": "激活梯度", - "wgrad": "权重梯度", - "optim": "优化器", - "re": "重计算", -} -CATEGORIES = { - "flops": "flops", - "flops_time": "flops时间", - "mem_accessed": "显存占用", - "mem_time": "访存时间", - "time": "耗时", -} - -PARSER = { - f"{l}_{s}_{c}": (lv, sv, cv) - for l, lv in LEVELS.items() - for s, sv in STAGES.items() - for c, cv in CATEGORIES.items() -} - -PARSER.update( - { - "fw_time": ("整体", "前向", "耗时"), - "bw_time": ("整体", "反向", "耗时"), - "optim_step_time": ("整体", "优化器", "耗时"), - "recompute_time": ("整体", "重计算", "耗时"), - "recomm_exposed_time": ("整体", "重计算通信", "耗时"), - "bubble_time": ("整体", "空泡", "耗时"), - "total_time": ("整体", "整体", "耗时"), - "compute_efficiency": ("整体", "计算效率", "-"), - "system_efficiency": ("整体", "系统效率", "-"), - "total_efficiency": ("整体", "总效率", "-"), - "sample_rate": ("整体", "采样率", "-"), - } -) - - -@dataclass -class result: - level: str - stage: str - category: str - name: str - value: Union[str, int, float] - - @staticmethod - def parse(key, val): - l, s, c = PARSER.get(key, (None, None, None)) - return result(l, s, c, key, val) - - -def human_format(num, round_to=1, use_tera=False): - magnitude = 0 - while abs(num) >= 1000: - magnitude += 1 - num = round(num / 1000.0, round_to) - formatter = ["", "K", "M", "G", "T"] if use_tera else ["", "K", "M", "B", "G"] - return "{:.{}f}{}".format(num, round_to, formatter[magnitude]) - - -def human_readable(num, round_to=1, formatter=None): - if isinstance(num, (str,)): - return num - for factor, suffix in formatter.items(): - if num >= factor: - return "{:.{}f}{}".format(num / factor, round_to, suffix) - return "{:.{}f}".format(num, round_to) - - -def human_readable_flops(num, round_to=1): - if not isinstance(num, (int, float)): - return num - return human_readable( - num, - round_to, - formatter={ - 1e15: "P", - 1e12: "T", - 1e6: "M", - 1e3: "K", - }, - ) - - -def human_readable_mem(num, round_to=1): - return human_readable( - num, - round_to, - formatter={ - 1e12: "T", - 1e9: "G", - 1e6: "M", - 1e3: "K", - }, - ) - - -def human_readable_num(num, round_to=1): - if not isinstance(num, (int, float)): - return num - return human_readable( - num, - round_to, - formatter={ - 1e12: "T", - 1e9: "B", - 1e6: "M", - 1e3: "K", - }, - ) - - -def human_readable_time(num, round_to=2): - if num >= 1: - return "{:.{}f} s".format(num, round_to) - if num >= 1e-3: - return "{:.{}f} ms".format(num * 1000, round_to) - if num >= 1e-6: - return "{:.{}f} ms".format(num * 1e6, round_to) - - -def make_summary(stats): - data = pd.DataFrame(result.parse(k, v) for k, v in stats.items()) - summary = pd.pivot_table( - data, index=["level", "stage"], columns="category", values="value" - ) - summary = summary.reindex( - [ - "前向", - "反向", - "激活梯度", - "权重梯度", - "重计算", - "重计算通信", - "优化器", - "空泡", - "整体", - "计算效率", - "系统效率", - "总效率", - "采样率", - ], - level=1, - ) - return summary.style.format( - {"flops": human_readable_flops, "显存占用": human_readable_mem} - ) - - -class HumanReadableFormatter: - def __init__(self, func): - self.func = func - - def __call__(self, *args, **kwargs): - return self.func(*args, **kwargs) - - def __mod__(self, other): - return self.func(other) - - -ReadableFLOPs = HumanReadableFormatter(human_readable_flops) -ReadableMem = HumanReadableFormatter(human_readable_mem) -ReadableNum = HumanReadableFormatter(human_readable_num) -ReadableTime = HumanReadableFormatter(human_readable_time) -NullFormatter = HumanReadableFormatter(lambda x: x) - - -def setup_sidebar(): - """设置公共侧边栏配置，返回 (app_json, sys_json, exe_json)""" - st.sidebar.title("参数配置") - with st.sidebar: - tabm, tabs, tabe = st.tabs(["模型", "系统", "执行"]) - - with tabm: - app_json = st.selectbox("模型配置", predefined.model_names) - app_json = make_json_conf(f"data/models/{app_json}") - with tabs: - sys_json = st.selectbox("系统配置", predefined.system_names) - sys_json = make_json_conf(f"data/systems/{sys_json}") - with tabe: - exe_json = st.selectbox("执行参数", predefined.execution_names) - exe_json = make_json_conf(f"data/examples/{exe_json}") - - return app_json, sys_json, exe_json - - -def setup_page(title="LLM训练计算器", icon=":eyeglasses:", layout="wide"): - """设置页面配置""" - st.set_page_config(page_title=title, page_icon=icon, layout=layout) - - -# ============================================================================ -# 共享 UI 组件 - 可复用的页面元素 -# ============================================================================ - - -def parallel_config_row( - ps: hp.scope, - show_gbs: bool = True, - show_mbs: bool = True, - show_seqlen: bool = True, - use_slider: bool = False, - mbs_param: str = "exe.microbatch_size", -) -> None: - """渲染并行配置行 (TP/PP/DP/GBS/MBS/SeqLen) - - Args: - ps: hyperparameter scope 对象 - show_gbs: 是否显示 global batch size - show_mbs: 是否显示 micro batch size - show_seqlen: 是否显示 sequence length - use_slider: 是否使用滑块（用于范围选择实验） - mbs_param: microbatch size 的参数路径 - """ - if use_slider: - tp_options = [1, 2, 4, 8, 16] - pp_options = [1, 2, 4, 8, 16] - dp_options = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024] - row( - ele( - "select_slider", - "TP", - value=(1, 1), - options=tp_options, - scope=ps, - param="exp.tp", - ), - ele( - "select_slider", - "PP", - value=(1, 1), - options=pp_options, - scope=ps, - param="exp.pp", - ), - ele( - "select_slider", - "DP", - value=(1, 1), - options=dp_options, - scope=ps, - param="exp.dp", - ), - ele("text", ""), - ( - ele( - "number_input", - "gbs", - value=ps.exe.batch_size | 1, - scope=ps, - param="exe.batch_size", - ) - if show_gbs - else ele("text", "") - ), - ( - ele( - "number_input", - "mbs", - value=ps.exe.microbatch_size | 1, - scope=ps, - param=mbs_param, - ) - if show_mbs - else ele("text", "") - ), - ele("text", ""), - ( - ele( - "number_input", - "seqlen", - value=ps.model.seq_size | 1, - scope=ps, - param="model.seq_size", - ) - if show_seqlen - else ele("text", "") - ), - ) - else: - dp_value = int( - (ps.exe.num_procs | 1) / (ps.exe.tensor_par | 1) / (ps.exe.pipeline_par | 1) - ) - row( - ele( - "number_input", - "TP", - value=ps.exe.tensor_par | 1, - scope=ps, - param="exe.tensor_par", - ), - ele( - "number_input", - "PP", - value=ps.exe.pipeline_par | 1, - scope=ps, - param="exe.pipeline_par", - ), - ele("number_input", "DP", value=dp_value, scope=ps, param="exe.data_par"), - ele("text", ""), - ( - ele( - "number_input", - "gbs", - value=ps.exe.batch_size | 1, - scope=ps, - param="exe.batch_size", - ) - if show_gbs - else ele("text", "") - ), - ( - ele( - "number_input", - "mbs", - value=ps.exe.microbatch_size | 1, - scope=ps, - param=mbs_param, - ) - if show_mbs - else ele("text", "") - ), - ele("text", ""), - ( - ele( - "number_input", - "seqlen", - value=ps.model.seq_size | 1, - scope=ps, - param="model.seq_size", - ) - if show_seqlen - else ele("text", "") - ), - ) - - -def config_popover(ps: hp.scope, label: str = "配置") -> dict: - """渲染配置弹窗 - - Args: - ps: hyperparameter scope 对象 - label: 弹窗按钮标签 - - Returns: - 包含配置选项的字典 - """ - config = {} - with st.popover(label, use_container_width=True): - config["use_humanreadable"] = st.checkbox("显示人类可读数字", True) - config["use_raw_output"] = st.checkbox("显示原始输出", False) - config["count_add"] = st.checkbox("FLOPS统计含加法") - ps.blueprinting.flops.count_add = config["count_add"] - return config - - -def page_header_with_config( - title: str, - ps: hp.scope, - use_slider: bool = False, - mbs_param: str = "exe.microbatch_size", -) -> dict: - """渲染页面头部，包含并行配置和配置弹窗 - - Args: - title: 页面标题 - ps: hyperparameter scope 对象 - use_slider: 是否使用滑块模式 - mbs_param: microbatch size 参数路径 - - Returns: - 配置字典 - """ - st.header(title, divider=True) - c1, c2 = st.columns([0.9, 0.1], vertical_alignment="bottom") - - with c1: - parallel_config_row(ps, use_slider=use_slider, mbs_param=mbs_param) - if not use_slider: - ps.exe.num_proc = ( - (ps.exe.tensor_par | 1) - * (ps.exe.pipeline_par | 1) - * (ps.exe.data_par | 1) - ) - - with c2: - config = config_popover(ps) - - return config - - -def transformer_config_expander(ps: hp.scope) -> dict: - """Transformer 模型参数配置展开区 - - Args: - ps: hyperparameter scope 对象 - - Returns: - 配置字典 - """ - config = {} - with st.expander("transformer参数", expanded=True): - r = st_row(3) - bias_flags = r.container() - emb_flags = r.container() - norm_flags = r.container() - - config.update( - { - "model.use_attn_bias": bias_flags.checkbox("attention投影bias", True), - "model.use_qkv_bias": bias_flags.checkbox("attention输入bias", True), - "model.use_mlp_bias": bias_flags.checkbox("MLP使用bias", True), - "model.type_posemb": emb_flags.selectbox( - "位置编码", ["learned", "rope"], 0 - ), - "model.vocab_size": emb_flags.number_input( - "词表大小", ps.model.vocab_size | 51200 - ), - "model.type_norm": norm_flags.selectbox("归一化层", ["LN", "RMS"], 0), - } - ) - - return config - - -def raw_output_section(stats: dict, summary_fn: Callable = None) -> None: - """渲染原始输出区域 - - Args: - stats: 统计数据字典 - summary_fn: 摘要生成函数 - """ - with st.expander("模拟器日志", expanded=False): - st.text(json.dumps(stats, indent=2)) - - if summary_fn: - with st.expander("模拟器输出", expanded=False): - st.dataframe(summary_fn(stats), use_container_width=True) - - -# ============================================================================ -# 布局组件 - 页面结构和视觉元素 -# ============================================================================ - - -def page_title(title: str, subtitle: str = None, icon: str = None): - """渲染页面标题区域 - - Args: - title: 主标题 - subtitle: 副标题/描述 - icon: 图标 emoji - """ - if icon: - st.markdown(f"# {icon} {title}") - else: - st.markdown(f"# {title}") - - if subtitle: - st.caption(subtitle) - - st.markdown("---") - - -def section_header(title: str, description: str = None): - """渲染章节标题 - - Args: - title: 章节标题 - description: 章节描述 - """ - st.markdown(f"### {title}") - if description: - st.caption(description) - - -def info_card(title: str, content: str, icon: str = "ℹ️"): - """渲染信息卡片 - - Args: - title: 卡片标题 - content: 卡片内容 - icon: 图标 - """ - st.markdown( - f""" -
-
- {icon} {title} -
-
- {content} -
-
- """, - unsafe_allow_html=True, - ) - - -def metric_card(label: str, value, delta=None, help_text: str = None): - """渲染指标卡片 - - Args: - label: 指标标签 - value: 指标值 - delta: 变化值 - help_text: 帮助文本 - """ - st.metric(label=label, value=value, delta=delta, help=help_text) - - -def metrics_row(*metrics): - """渲染一行指标 - - Args: - metrics: [(label, value, delta?, help?), ...] 列表 - """ - cols = st.columns(len(metrics)) - for col, metric in zip(cols, metrics): - with col: - if len(metric) == 2: - metric_card(metric[0], metric[1]) - elif len(metric) == 3: - metric_card(metric[0], metric[1], metric[2]) - else: - metric_card(metric[0], metric[1], metric[2], metric[3]) - - -def two_column_layout(left_ratio: float = 0.5): - """创建两列布局 - - Args: - left_ratio: 左列占比 - - Returns: - (left_col, right_col) 元组 - """ - return st.columns([left_ratio, 1 - left_ratio]) diff --git a/src/blueprinting/util.py b/src/blueprinting/util.py deleted file mode 100755 index 2520179..0000000 --- a/src/blueprinting/util.py +++ /dev/null @@ -1,4 +0,0 @@ -def pick(en, a, b): - if en: - return a - return b diff --git a/src/blueprinting/validation/legacy/__init__.py b/src/blueprinting/validation/legacy/__init__.py deleted file mode 100644 index 85cf98b..0000000 --- a/src/blueprinting/validation/legacy/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Retained oracle-only reproductions that do not exercise canonical derivation.""" diff --git a/src/blueprinting/validation/legacy/seqsel_fig1.py b/src/blueprinting/validation/legacy/seqsel_fig1.py deleted file mode 100644 index 77832ac..0000000 --- a/src/blueprinting/validation/legacy/seqsel_fig1.py +++ /dev/null @@ -1,119 +0,0 @@ -"""Legacy Calculon reproduction of SeqSel figure 1. - -This compatibility check does not exercise Blueprinting's canonical derivation -path; the strict production gate lives in :mod:`blueprinting.validation.calculon`. -""" - -import logging - -import hyperparameter as hp -import pandas as pd - -# Calculon is used here for validation comparison only -import blueprinting.io as io -from blueprinting.types import Execution, Model -from calculon.llm import Llm -from calculon.llm import System as CalculonSystem - -kProfile = { - "megatron-22B": { - "none": {"par_opt": 45.5625, "act": 59.25}, - "seqsel": {"par_opt": 45.5625, "act": 9.5625}, - }, - "gpt3-175B": { - "none": {"par_opt": 45.5625, "act": 66.84375}, - "seqsel": {"par_opt": 45.5625, "act": 12.3515625}, - }, - "turing-530B": { - "none": {"par_opt": 31.640625, "act": 114.0234375}, - "seqsel": {"par_opt": 31.640625, "act": 23.076171875}, - }, - "megatron-1T": { - "none": {"par_opt": 32.958984375, "act": 131.25}, - "seqsel": {"par_opt": 32.958984375, "act": 26.5625}, - }, -} - -mem_usage = pd.DataFrame.from_records( - [ - { - "model": model, - "system": "a100_80e", - "mode": mode, - "w+opt mem(GiB)": kProfile[model][mode]["par_opt"], - "act mem(GiB)": kProfile[model][mode]["act"], - } - for model in kProfile - for mode in kProfile[model] - ] -) - - -def seqsel_fig1(show=False): - models = mem_usage["model"].unique() - systems = mem_usage["system"].unique() - modes = mem_usage["mode"].unique() - - records = [] - - for m, s, e in [(m, s, e) for m in models for s in systems for e in modes]: - app_json = f"data/models/{m}.json" - sys_json = f"data/systems/{s}.json" - exe_json = f"data/validation/seqsel/fig1/{m}_{e}.json" - - logger = logging.getLogger() - - app_json = io.read_json_file(app_json) - sys_json = io.read_json_file(sys_json) - exe_json = io.read_json_file(exe_json) - - with hp.scope(app=app_json, sys=sys_json, exe=exe_json) as ps: - app = Model(ps.app) - exe = Execution(ps.exe) - # Use calculon's System for validation - syst = CalculonSystem(sys_json) - - model = Llm(app, logger) - model.compile(syst, exe) - model.run(syst) - stats = model.get_stats_json(False) - act_par_opt = (stats["weight_space"] + stats["weight_grad_space"] + stats["optimizer_space"]) / (1024**3) - act_act = stats["act_space"] / (1024**3) - records += [ - { - "model": m, - "system": s, - "mode": e, - "w+opt mem(GiB)": act_par_opt, - "act mem(GiB)": act_act, - } - ] - df = pd.DataFrame.from_records(records) - result = ( - mem_usage.set_index(["model", "system", "mode"]) - .join( - df.set_index(["model", "system", "mode"]), - on=["model", "system", "mode"], - lsuffix="[act]", - rsuffix="[pred]", - ) - .reset_index() - ) - - result["w+opt mem/rtol"] = (result["w+opt mem(GiB)[act]"] - result["w+opt mem(GiB)[pred]"]).abs() / result[ - "w+opt mem(GiB)[act]" - ] - result["act mem/rtol"] = (result["act mem(GiB)[act]"] - result["act mem(GiB)[pred]"]).abs() / result[ - "act mem(GiB)[act]" - ] - - return ( - result.style.format( - { - "w+opt mem/rtol": lambda x: "%.2f%%" % (100 * x), - "act mem/rtol": lambda x: "%.2f%%" % (100 * x), - } - ) - if show - else result - ) diff --git a/src/blueprinting/validation/legacy/seqsel_fig7.py b/src/blueprinting/validation/legacy/seqsel_fig7.py deleted file mode 100644 index fe2c63f..0000000 --- a/src/blueprinting/validation/legacy/seqsel_fig7.py +++ /dev/null @@ -1,133 +0,0 @@ -"""Legacy Calculon reproduction of SeqSel figure 7. - -This compatibility check does not exercise Blueprinting's canonical derivation -path; the strict production gate lives in :mod:`blueprinting.validation.calculon`. -""" - -import logging - -import hyperparameter as hp -import pandas as pd - -import blueprinting.io as io -from blueprinting.types import Execution, Model - -# Calculon is used here for validation comparison only -from calculon.llm import Llm -from calculon.llm import System as CalculonSystem - -kProfile = { - "megatron-22B": { - "none": 100.00, - "seq": 66.84, - "sel": 49.42, - "seqsel": 16.18, - "full": 7.64, - }, - "gpt3-175B": { - "none": 100.00, - "seq": 62.04, - "sel": 56.53, - "seqsel": 18.49, - "full": 8.71, - }, - "turing-530B": { - "none": 100.00, - "seq": 58.31, - "sel": 62.04, - "seqsel": 20.27, - "full": 9.42, - }, - "megatron-1T": { - "none": 100.00, - "seq": 58.31, - "sel": 62.04, - "seqsel": 20.27, - "full": 9.42, - }, -} - - -mem_usage = pd.DataFrame.from_records( - [ - { - "model": model, - "system": "a100_80e", - "mode": mode, - "act mem(%)": kProfile[model][mode], - } - for model in kProfile - for mode in kProfile[model] - ] -) - - -def seqsel_fig7(show=False): - models = mem_usage["model"].unique() - systems = mem_usage["system"].unique() - modes = mem_usage["mode"].unique() - - records = [] - - for m, s, e in [(m, s, e) for m in models for s in systems for e in modes]: - app_json = f"data/models/{m}.json" - sys_json = f"data/systems/{s}.json" - exe_json = f"data/validation/seqsel/fig7/{m}_{e}.json" - - logger = logging.getLogger() - - app_json = io.read_json_file(app_json) - sys_json = io.read_json_file(sys_json) - exe_json = io.read_json_file(exe_json) - - with hp.scope(app=app_json, sys=sys_json, exe=exe_json) as ps: - app = Model(ps.app) - exe = Execution(ps.exe) - # Use calculon's System for validation - syst = CalculonSystem(sys_json) - - model = Llm(app, logger) - model.compile(syst, exe) - model.run(syst) - stats = model.get_stats_json(False) - act_act = stats["act_space"] + stats["act_checkpoint_size"] - records += [ - { - "model": m, - "system": s, - "mode": e, - "act mem(%)": act_act, - } - ] - df = pd.DataFrame.from_records(records) - selected = df[df["mode"] == "none"] - for _, row in df.iterrows(): - x = selected[(selected.model == row.model) & (selected.system == row.system)] - df.loc[ - (df.model == row.model) & (df.system == row.system) & (df["mode"] == row["mode"]), - "act mem(%)", - ] = 100 * row["act mem(%)"] / x.iloc[0]["act mem(%)"] - result = ( - mem_usage.set_index(["model", "system", "mode"]) - .join( - df.set_index(["model", "system", "mode"]), - on=["model", "system", "mode"], - lsuffix="[act]", - rsuffix="[pred]", - ) - .reset_index() - ) - - result["act mem/rtol"] = (result["act mem(%)[act]"] - result["act mem(%)[pred]"]).abs() / result["act mem(%)[act]"] - - return ( - result.style.format( - { - "act mem/rtol": lambda x: "%.2f%%" % (100 * x), - "act mem(%)[act]": "{:.2f}%", - "act mem(%)[pred]": "{:.2f}%", - } - ) - if show - else result - ) diff --git a/src/blueprinting/validation/legacy/seqsel_tab5.py b/src/blueprinting/validation/legacy/seqsel_tab5.py deleted file mode 100644 index 35f6ede..0000000 --- a/src/blueprinting/validation/legacy/seqsel_tab5.py +++ /dev/null @@ -1,93 +0,0 @@ -"""Legacy Calculon reproduction of SeqSel table 5. - -This compatibility check does not exercise Blueprinting's canonical derivation -path; the strict production gate lives in :mod:`blueprinting.validation.calculon`. -""" - -import logging - -import hyperparameter as hp -import pandas as pd - -import blueprinting.io as io -from blueprinting.types import Execution, Model - -# Calculon is used here for validation comparison only -from calculon.llm import Llm -from calculon.llm import System as CalculonSystem - -kProfile = { - "megatron-22B": {"full": 1.42, "seqsel": 1.10}, - "gpt3-175B": {"full": 18.13, "seqsel": 13.75}, - "turing-530B": {"full": 49.05, "seqsel": 37.83}, - "megatron-1T": {"full": 94.42, "seqsel": 71.49}, -} - -iter_time = pd.DataFrame.from_records( - [ - { - "model": model, - "system": "a100_80g", - "mode": mode, - "iter time(s)": kProfile[model][mode], - } - for model in kProfile - for mode in kProfile[model] - ] -) - - -def seqsel_tab5(show=False): - models = iter_time["model"].unique() - systems = iter_time["system"].unique() - modes = iter_time["mode"].unique() - - records = [] - - for m, s, e in [(m, s, e) for m in models for s in systems for e in modes]: - app_json = f"data/models/{m}.json" - sys_json = f"data/systems/{s}.json" - exe_json = f"data/validation/seqsel/tab5/{m}_{e}.json" - - logger = logging.getLogger() - - app_json = io.read_json_file(app_json) - sys_json = io.read_json_file(sys_json) - exe_json = io.read_json_file(exe_json) - - with hp.scope(app=app_json, sys=sys_json, exe=exe_json) as ps: - app = Model(ps.app) - exe = Execution(ps.exe) - # Use calculon's System for validation - syst = CalculonSystem(sys_json) - - model = Llm(app, logger) - model.compile(syst, exe) - model.run(syst) - stats = model.get_stats_json(False) - act_act = stats["total_time"] - records += [ - { - "model": m, - "system": s, - "mode": e, - "iter time(s)": act_act, - } - ] - df = pd.DataFrame.from_records(records) - result = ( - iter_time.set_index(["model", "system", "mode"]) - .join( - df.set_index(["model", "system", "mode"]), - on=["model", "system", "mode"], - lsuffix="[act]", - rsuffix="[pred]", - ) - .reset_index() - ) - - result["iter time/rtol"] = (result["iter time(s)[act]"] - result["iter time(s)[pred]"]).abs() / result[ - "iter time(s)[act]" - ] - - return result.style.format({"iter time/rtol": lambda x: "%.2f%%" % (100 * x)}) if show else result diff --git a/src/blueprinting/workbench/nicegui_ui.py b/src/blueprinting/workbench/nicegui_ui.py index 8155131..ad64a76 100644 --- a/src/blueprinting/workbench/nicegui_ui.py +++ b/src/blueprinting/workbench/nicegui_ui.py @@ -618,7 +618,6 @@ def build(self) -> None: ui.colors(primary="#2563eb", secondary="#2563eb", positive="#15803d", warning="#b45309", negative="#b91c1c") ui.dark_mode(False) ui.page_title("Blueprinting · 硬件架构工作台") - self._build_legacy_dialog() with ui.left_drawer(value=True, bordered=False) as self.sidebar: self.sidebar.props("width=288 breakpoint=980").classes("bp-sidebar") @@ -672,9 +671,6 @@ def build(self) -> None: ui.element("span").classes("bp-service-dot") ui.label("本地服务可用").classes("bp-sidebar-meta") ui.label("Formal plan · Numerical evidence").classes("bp-sidebar-meta bp-mono") - ui.button("Legacy 工具", icon="history", on_click=self.legacy_dialog.open).props( - "flat no-caps align=left" - ).classes("bp-sidebar-legacy w-full") with ( ui.dialog() as self.config_dialog, @@ -708,20 +704,6 @@ def _toggle_sidebar(self) -> None: if hasattr(self, "sidebar"): self.sidebar.toggle() - def _build_legacy_dialog(self) -> None: - with ui.dialog() as self.legacy_dialog, ui.card().classes("bp-card").style("width: 560px; max-width: 92vw"): - with ui.row().classes("items-center gap-3"): - ui.icon("inventory_2", size="26px", color="secondary") - with ui.column().classes("gap-0"): - ui.label("Calculon / Streamlit Legacy").classes("bp-card-title") - ui.label("旧工具保持隔离，不参与 Blueprinting 主分析路径。 ").classes("bp-card-copy") - ui.separator().classes("my-2") - ui.label("需要旧 Calculon 工具时，请单独启动：").classes("text-sm") - ui.code("uv run streamlit run streamlit_app.py", language="bash").classes("bp-code") - with ui.row().classes("w-full justify-end gap-2"): - ui.link("打开 localhost:8501", "http://127.0.0.1:8501", new_tab=True).classes("text-secondary") - ui.button("关闭", on_click=self.legacy_dialog.close).props("flat no-caps") - def _select_mode(self, mode: WorkbenchMode) -> None: if self.busy or mode is self.mode: return diff --git a/src/blueprinting/workbench/streamlit_ui.py b/src/blueprinting/workbench/streamlit_ui.py deleted file mode 100644 index 6b3636d..0000000 --- a/src/blueprinting/workbench/streamlit_ui.py +++ /dev/null @@ -1,677 +0,0 @@ -"""Reusable Streamlit components backed only by the Blueprinting service.""" - -from __future__ import annotations - -import inspect -import json -from dataclasses import dataclass -from typing import Any - -import pandas as pd -import streamlit as st - -from blueprinting.analysis import CalibrationMode -from blueprinting.application import ( - AnalysisDiagnostic, - AnalysisDraft, - AnalysisOutcome, - BlueprintingService, - DiagnosticLevel, - SweepReport, - SweepRequest, -) - -from .catalog import ConfigCatalog, default_catalog - -_COMPILER_DTYPES = ("float16", "bfloat16", "float32", "float8") -_PARALLEL_OPTIONS = (1, 2, 4, 8, 12, 16, 24, 32, 48, 64, 96, 128) - - -def _stretch(widget: Any) -> dict[str, Any]: - """Use the current width API while retaining Streamlit 1.40 compatibility.""" - - if "width" in inspect.signature(widget).parameters: - return {"width": "stretch"} - return {"use_container_width": True} - - -@dataclass(frozen=True) -class _PresetSelection: - catalog: ConfigCatalog - model_name: str - execution_name: str - hardware_name: str - model_data: dict[str, Any] - execution_data: dict[str, Any] - hardware_data: dict[str, Any] - - @property - def namespace(self) -> str: - return f"{self.model_name}:{self.execution_name}:{self.hardware_name}" - - -def setup_workbench_page(title: str, icon: str) -> None: - st.set_page_config(page_title=f"Blueprinting · {title}", page_icon=icon, layout="wide") - st.title(f"{icon} {title}") - - -def _preferred_index(names: tuple[str, ...], preferred: str) -> int: - return names.index(preferred) if preferred in names else 0 - - -def _preset_selection(key_prefix: str) -> _PresetSelection: - catalog = default_catalog() - model_names = catalog.names("models") - execution_names = catalog.names("examples") - hardware_names = catalog.names("systems") - with st.sidebar: - st.header("分析输入") - model_name = st.selectbox( - "模型预设", - model_names, - index=_preferred_index(model_names, "gpt3-175B.json"), - key=f"{key_prefix}.preset.model", - ) - hardware_name = st.selectbox( - "硬件证据", - hardware_names, - index=_preferred_index(hardware_names, "a100_80g.json"), - key=f"{key_prefix}.preset.hardware", - ) - execution_name = st.selectbox( - "策略模板", - execution_names, - index=0, - key=f"{key_prefix}.preset.execution", - ) - return _PresetSelection( - catalog=catalog, - model_name=model_name, - execution_name=execution_name, - hardware_name=hardware_name, - model_data=catalog.load("models", model_name), - execution_data=catalog.load("examples", execution_name), - hardware_data=catalog.load("systems", hardware_name), - ) - - -def _model_fields(selection: _PresetSelection, key_prefix: str) -> dict[str, Any]: - model = selection.model_data - namespace = f"{key_prefix}.{selection.namespace}.model" - with st.expander("模型语义", expanded=False): - hidden = st.number_input("Hidden size", min_value=1, value=int(model["hidden"]), key=f"{namespace}.hidden") - feedforward = st.number_input( - "Feed-forward size", - min_value=1, - value=int(model["feedforward"]), - key=f"{namespace}.feedforward", - ) - sequence = st.number_input( - "Sequence length", - min_value=1, - value=int(model["seq_size"]), - key=f"{namespace}.sequence", - ) - heads = st.number_input( - "Attention heads", - min_value=1, - value=int(model["attn_heads"]), - key=f"{namespace}.heads", - ) - head_size = st.number_input( - "Attention head size", - min_value=1, - value=int(model["attn_size"]), - key=f"{namespace}.head_size", - ) - blocks = st.number_input( - "Transformer blocks", - min_value=1, - value=int(model["num_blocks"]), - key=f"{namespace}.blocks", - ) - return { - "hidden": int(hidden), - "feedforward": int(feedforward), - "seq_size": int(sequence), - "attn_heads": int(heads), - "attn_size": int(head_size), - "num_blocks": int(blocks), - } - - -def _supported_datatypes(selection: _PresetSelection) -> tuple[str, ...]: - matrix = set(selection.hardware_data.get("matrix", {})) - vector = set(selection.hardware_data.get("vector", {})) - supported = tuple(item for item in _COMPILER_DTYPES if item in matrix and item in vector) - if not supported: - raise ValueError(f"硬件预设 {selection.hardware_name} 没有同时定义 matrix/vector datatype") - return supported - - -def _strategy_fields( - selection: _PresetSelection, - key_prefix: str, - *, - include_parallelism: bool, -) -> tuple[dict[str, Any], tuple[int, int, int] | None]: - execution = dict(selection.execution_data) - namespace = f"{key_prefix}.{selection.namespace}.execution" - parallelism = None - if include_parallelism: - st.markdown("**并行策略**") - col1, col2, col3 = st.columns(3) - with col1: - tp = st.number_input( - "Tensor parallel", - min_value=1, - value=int(execution["tensor_par"]), - key=f"{namespace}.tp", - ) - with col2: - pp = st.number_input( - "Pipeline parallel", - min_value=1, - value=int(execution["pipeline_par"]), - key=f"{namespace}.pp", - ) - with col3: - dp = st.number_input( - "Data parallel", - min_value=1, - value=int(execution["data_par"]), - key=f"{namespace}.dp", - ) - parallelism = (int(tp), int(pp), int(dp)) - st.caption(f"World size 由并行度推导：{int(tp) * int(pp) * int(dp):,}") - - st.markdown("**训练负载**") - col1, col2 = st.columns(2) - with col1: - global_batch = st.number_input( - "Global batch size", - min_value=1, - value=int(execution["batch_size"]), - key=f"{namespace}.global_batch", - ) - with col2: - microbatch = st.number_input( - "Microbatch size", - min_value=1, - value=int(execution["microbatch_size"]), - key=f"{namespace}.microbatch", - ) - - dtypes = _supported_datatypes(selection) - current_dtype = execution.get("datatype", dtypes[0]) - datatype = st.selectbox( - "Datatype", - dtypes, - index=dtypes.index(current_dtype) if current_dtype in dtypes else 0, - key=f"{namespace}.datatype", - ) - recompute_options = ("none", "attn_only", "full") - recompute = st.selectbox( - "Activation recompute", - recompute_options, - index=recompute_options.index(execution.get("activation_recompute", "none")), - key=f"{namespace}.recompute", - ) - communication_options = ("ar", "rs_ag") - current_communication = execution.get("tensor_par_comm_type", "ar") - communication = st.selectbox( - "TP communication", - communication_options, - index=communication_options.index(current_communication) if current_communication in communication_options else 0, - key=f"{namespace}.communication", - ) - interleaving = st.number_input( - "Pipeline interleaving", - min_value=1, - value=int(execution.get("pipeline_interleaving", 1)), - key=f"{namespace}.interleaving", - ) - optimizer_sharding = st.checkbox( - "Optimizer sharding", - value=bool(execution.get("optimizer_sharding", False)), - key=f"{namespace}.optimizer_sharding", - ) - - network_count = len(selection.hardware_data.get("networks", ())) - network_options = tuple(range(network_count)) - if not network_options: - raise ValueError(f"硬件预设 {selection.hardware_name} 没有网络层级") - with st.expander("网络映射", expanded=False): - tp_network = st.selectbox( - "TP network tier", - network_options, - index=min(int(execution.get("tensor_par_net", 0)), network_count - 1), - key=f"{namespace}.tp_network", - ) - pp_network = st.selectbox( - "PP network tier", - network_options, - index=min(int(execution.get("pipeline_par_net", 0)), network_count - 1), - key=f"{namespace}.pp_network", - ) - dp_network = st.selectbox( - "DP network tier", - network_options, - index=min(int(execution.get("data_par_net", 0)), network_count - 1), - key=f"{namespace}.dp_network", - ) - - execution.update( - { - "batch_size": int(global_batch), - "microbatch_size": int(microbatch), - "datatype": datatype, - "activation_recompute": recompute, - "tensor_par_comm_type": communication, - "pipeline_interleaving": int(interleaving), - "optimizer_sharding": bool(optimizer_sharding), - "tensor_par_net": int(tp_network), - "pipeline_par_net": int(pp_network), - "data_par_net": int(dp_network), - "attention_type": "multihead", - "tensor_par_overlap": "none", - "data_par_overlap": False, - "weight_offload": False, - "activations_offload": False, - "optimizer_offload": False, - "training": True, - } - ) - if parallelism is not None: - execution.update( - { - "tensor_par": parallelism[0], - "pipeline_par": parallelism[1], - "data_par": parallelism[2], - "num_procs": parallelism[0] * parallelism[1] * parallelism[2], - } - ) - return execution, parallelism - - -def _calibration_field(selection: _PresetSelection, key_prefix: str) -> CalibrationMode: - namespace = f"{key_prefix}.{selection.namespace}.calibration" - labels = { - "系统证据曲线": CalibrationMode.SYSTEM_EVIDENCE, - "理论峰值基线": CalibrationMode.PEAK_ONLY, - } - selected = st.radio( - "估算证据", - tuple(labels), - horizontal=True, - key=namespace, - ) - return labels[selected] - - -def analysis_form(key_prefix: str, submit_label: str = "运行 Blueprinting 分析") -> AnalysisDraft | None: - selection = _preset_selection(key_prefix) - with st.sidebar, st.form(f"{key_prefix}.analysis_form"): - model_data = _model_fields(selection, key_prefix) - execution_data, _ = _strategy_fields(selection, key_prefix, include_parallelism=True) - calibration = _calibration_field(selection, key_prefix) - submitted = st.form_submit_button(submit_label, type="primary", **_stretch(st.form_submit_button)) - if not submitted: - return None - return AnalysisDraft.from_mappings( - model_name=selection.model_name.removesuffix(".json"), - model_data=model_data, - execution_name=selection.execution_name, - execution_data=execution_data, - hardware_name=selection.hardware_name.removesuffix(".json"), - hardware_data=selection.hardware_data, - calibration_mode=calibration, - ) - - -def sweep_form(key_prefix: str) -> SweepRequest | None: - selection = _preset_selection(key_prefix) - execution = selection.execution_data - with st.sidebar, st.form(f"{key_prefix}.sweep_form"): - model_data = _model_fields(selection, key_prefix) - execution_data, _ = _strategy_fields(selection, key_prefix, include_parallelism=False) - calibration = _calibration_field(selection, key_prefix) - st.markdown("**候选空间**") - options = tuple( - sorted( - set( - _PARALLEL_OPTIONS - + ( - int(execution["tensor_par"]), - int(execution["pipeline_par"]), - int(execution["data_par"]), - ) - ) - ) - ) - tp_values = st.multiselect( - "Tensor parallel candidates", - options, - default=[int(execution["tensor_par"])], - key=f"{key_prefix}.{selection.namespace}.sweep.tp", - ) - pp_values = st.multiselect( - "Pipeline parallel candidates", - options, - default=[int(execution["pipeline_par"])], - key=f"{key_prefix}.{selection.namespace}.sweep.pp", - ) - dp_values = st.multiselect( - "Data parallel candidates", - options, - default=[int(execution["data_par"])], - key=f"{key_prefix}.{selection.namespace}.sweep.dp", - ) - count = len(tp_values) * len(pp_values) * len(dp_values) - st.caption(f"候选数量：{count} / 128") - submitted = st.form_submit_button( - "探索策略空间", - type="primary", - **_stretch(st.form_submit_button), - ) - if not submitted: - return None - if not tp_values or not pp_values or not dp_values: - st.sidebar.error("TP、PP、DP 候选集合不能为空。") - return None - base_tp = int(execution["tensor_par"]) - base_pp = int(execution["pipeline_par"]) - base_dp = int(execution["data_par"]) - execution_data.update( - { - "tensor_par": base_tp, - "pipeline_par": base_pp, - "data_par": base_dp, - "num_procs": base_tp * base_pp * base_dp, - } - ) - draft = AnalysisDraft.from_mappings( - model_name=selection.model_name.removesuffix(".json"), - model_data=model_data, - execution_name=selection.execution_name, - execution_data=execution_data, - hardware_name=selection.hardware_name.removesuffix(".json"), - hardware_data=selection.hardware_data, - calibration_mode=calibration, - ) - try: - return SweepRequest( - base=draft, - tensor_parallel=tuple(int(item) for item in tp_values), - pipeline_parallel=tuple(int(item) for item in pp_values), - data_parallel=tuple(int(item) for item in dp_values), - ) - except ValueError as error: - st.sidebar.error(str(error)) - return None - - -@st.cache_data(show_spinner=False) -def cached_analysis(draft: AnalysisDraft) -> AnalysisOutcome: - return BlueprintingService().analyze(draft) - - -@st.cache_data(show_spinner=False) -def cached_sweep(request: SweepRequest) -> SweepReport: - return BlueprintingService().sweep(request) - - -def remember(key: str, value: Any) -> Any: - st.session_state[key] = value - return value - - -def recalled(key: str) -> Any | None: - return st.session_state.get(key) - - -def format_seconds(value: float) -> str: - if value >= 1: - return f"{value:.3f} s" - if value >= 1e-3: - return f"{value * 1e3:.3f} ms" - if value >= 1e-6: - return f"{value * 1e6:.3f} µs" - return f"{value * 1e9:.3f} ns" - - -def format_bytes(value: int | float) -> str: - number = float(value) - units = ("B", "KiB", "MiB", "GiB", "TiB", "PiB") - for unit in units: - if abs(number) < 1024 or unit == units[-1]: - return f"{number:.2f} {unit}" - number /= 1024 - raise AssertionError("byte formatter did not terminate") - - -def format_count(value: int | float) -> str: - number = float(value) - for scale, suffix in ((1e15, "P"), (1e12, "T"), (1e9, "G"), (1e6, "M"), (1e3, "K")): - if abs(number) >= scale: - return f"{number / scale:.2f}{suffix}" - return f"{number:.0f}" - - -def render_diagnostics(diagnostics: tuple[AnalysisDiagnostic, ...]) -> None: - for diagnostic in diagnostics: - location = ".".join(diagnostic.path) - suffix = f" · `{location}`" if location else "" - message = f"**{diagnostic.code}**{suffix} — {diagnostic.message}" - if diagnostic.hint: - message += f"\n\n建议：{diagnostic.hint}" - if diagnostic.level is DiagnosticLevel.ERROR: - st.error(message) - elif diagnostic.level is DiagnosticLevel.WARNING: - st.warning(message) - else: - st.info(message) - - -def render_analysis_summary(outcome: AnalysisOutcome) -> None: - render_diagnostics(outcome.diagnostics) - report = outcome.report - if report is None: - st.caption(f"Request digest: `{outcome.request_digest}`") - return - - if report.feasible: - st.success("该候选在当前解析式内存模型下可放入单设备容量。") - else: - st.warning("该候选完成了分析，但不满足设备内存容量约束。") - - columns = st.columns(5) - metrics = ( - ("迭代延迟", format_seconds(report.total_seconds)), - ("Token/s", format_count(report.total_tokens_per_second)), - ("Token/s/设备", format_count(report.tokens_per_second_per_device)), - ("单设备内存", format_bytes(report.memory["total"])), - ("主导项", report.bottleneck.replace("_", " ")), - ) - for column, (label, value) in zip(columns, metrics): - column.metric(label, value) - - tab_latency, tab_memory, tab_workload, tab_evidence = st.tabs( - ["延迟分解", "内存分解", "工作量事实", "证据与边界"] - ) - with tab_latency: - latency = pd.DataFrame( - ((name.replace("_", " "), value) for name, value in report.latency.items()), - columns=("component", "seconds"), - ).set_index("component") - st.bar_chart(latency, horizontal=True) - st.dataframe( - latency.reset_index().assign(display=lambda frame: frame["seconds"].map(format_seconds)), - hide_index=True, - **_stretch(st.dataframe), - ) - with tab_memory: - memory = pd.DataFrame( - ( - (name.replace("_", " "), value) - for name, value in report.memory.items() - if name not in {"total", "capacity"} - ), - columns=("component", "bytes"), - ).set_index("component") - st.bar_chart(memory, horizontal=True) - used = report.memory["total"] / report.memory["capacity"] - st.progress(min(float(used), 1.0), text=f"容量使用率 {used:.1%}") - with tab_workload: - workload = report.workload.to_dict() - rows = [ - {"事实": "Portable tasks", "值": f"{workload['task_count']:,}"}, - {"事实": "Compute tasks", "值": f"{workload['compute_task_count']:,}"}, - {"事实": "Collective tasks", "值": f"{workload['collective_task_count']:,}"}, - {"事实": "Operations", "值": format_count(workload["operations"])}, - {"事实": "Read bytes", "值": format_bytes(workload["read_bytes"])}, - {"事实": "Write bytes", "值": format_bytes(workload["write_bytes"])}, - {"事实": "Message bytes", "值": format_bytes(workload["message_bytes"])}, - ] - st.dataframe(pd.DataFrame(rows), hide_index=True, **_stretch(st.dataframe)) - with tab_evidence: - evidence = report.evidence.to_dict() - st.json(evidence) - st.markdown("**当前实现边界**") - for limitation in report.limitations: - st.markdown(f"- {limitation}") - st.caption( - f"Plan `{report.plan_digest}` · Evidence `{report.evidence_revision}` · " - f"Request `{report.request_digest}`" - ) - - -def stage_dataframe(outcome: AnalysisOutcome) -> pd.DataFrame: - if outcome.report is None: - return pd.DataFrame() - return pd.DataFrame( - { - "stage": stage.stage, - "label": stage.label, - "pass": stage.pass_name, - "schema": stage.schema, - "nodes": stage.node_count, - "values/buffers": stage.value_count, - "lowering_ms": stage.duration_ns / 1e6, - "valid": stage.valid, - "digest": stage.digest, - } - for stage in outcome.report.stages - ) - - -def task_dataframe(outcome: AnalysisOutcome) -> pd.DataFrame: - if outcome.report is None: - return pd.DataFrame() - return pd.DataFrame( - { - "operation": task.operation, - "phase": task.phase, - "engine": task.engine, - "kind": task.kind, - "ops": task.operations, - "read_bytes": task.read_bytes, - "write_bytes": task.write_bytes, - "message_bytes": task.message_bytes, - "compute_s": task.compute_seconds, - "memory_s": task.memory_seconds, - "network_s": task.network_seconds, - "total_s": task.total_seconds, - "dependencies": len(task.dependencies), - "concurrency_group": task.concurrency_group, - "task_id": task.task_id, - } - for task in outcome.report.tasks - ) - - -def render_ir_explorer(outcome: AnalysisOutcome) -> None: - render_diagnostics(outcome.diagnostics) - report = outcome.report - if report is None: - return - st.subheader("推导边界") - st.dataframe(stage_dataframe(outcome), hide_index=True, **_stretch(st.dataframe)) - tabs = st.tabs([stage.label for stage in report.stages]) - for tab, stage in zip(tabs, report.stages): - with tab: - cols = st.columns(4) - cols[0].metric("节点", stage.node_count) - cols[1].metric("值 / Buffer", stage.value_count) - cols[2].metric("Lowering 耗时", format_seconds(stage.duration_ns / 1e9)) - cols[3].metric("Verifier", "通过" if stage.valid else "失败") - st.caption(f"`{stage.schema}` · `{stage.digest}`") - render_diagnostics(stage.diagnostics) - snapshot = json.loads(stage.snapshot_json) - with st.expander("Canonical IR snapshot", expanded=False): - st.json(snapshot) - st.download_button( - "下载 snapshot", - data=json.dumps(snapshot, indent=2, ensure_ascii=False), - file_name=f"{stage.stage}-{stage.digest[:12]}.json", - mime="application/json", - key=f"download.{stage.digest}", - ) - st.subheader("Portable task audit") - st.dataframe(task_dataframe(outcome), hide_index=True, **_stretch(st.dataframe)) - - -def sweep_dataframe(report: SweepReport) -> pd.DataFrame: - return pd.DataFrame( - { - "tp": case.tensor_parallel, - "pp": case.pipeline_parallel, - "dp": case.data_parallel, - "world_size": case.world_size, - "status": case.status, - "feasible": case.feasible, - "pareto": case.pareto, - "latency_s": case.total_seconds, - "memory_gib": case.memory_bytes / 1024**3 if case.memory_bytes is not None else None, - "tokens_s_device": case.tokens_per_second_per_device, - "bottleneck": case.bottleneck, - "diagnostic": "; ".join(item.message for item in case.diagnostics), - "request_digest": case.request_digest, - } - for case in report.cases - ) - - -def render_sweep(report: SweepReport) -> None: - data = sweep_dataframe(report) - columns = st.columns(4) - columns[0].metric("候选", len(report.cases)) - columns[1].metric("成功", report.succeeded_count) - columns[2].metric("可行", report.feasible_count) - columns[3].metric("Pareto", int(data["pareto"].sum())) - - successful = data[(data["status"] == "success") & data["latency_s"].notna()] - if not successful.empty: - st.subheader("延迟—内存空间") - st.scatter_chart( - successful, - x="memory_gib", - y="latency_s", - color="pareto", - size="world_size", - ) - st.subheader("全部候选") - st.dataframe( - data.sort_values(["pareto", "feasible", "latency_s"], ascending=[False, False, True]), - hide_index=True, - **_stretch(st.dataframe), - ) - invalid = data[data["status"] != "success"] - if not invalid.empty: - with st.expander(f"无效候选诊断（{len(invalid)}）", expanded=False): - st.dataframe( - invalid[["tp", "pp", "dp", "diagnostic", "request_digest"]], - hide_index=True, - **_stretch(st.dataframe), - ) - st.caption(f"Sweep request `{report.request_digest}`") diff --git a/streamlit_app.py b/streamlit_app.py deleted file mode 100755 index 59f4538..0000000 --- a/streamlit_app.py +++ /dev/null @@ -1,52 +0,0 @@ -"""Legacy Streamlit surface for Calculon and transitional Blueprinting pages. - -The primary workbench is now launched with ``blueprinting-workbench``. Existing -Calculon pages remain available here as an independent baseline and are not -part of the Blueprinting product analysis path. -""" - -import streamlit as st - -# ============================================================================ -# 页面导航配置 -# ============================================================================ -pg = st.navigation( - { - # The primary Blueprinting workflow stays directly visible. Secondary - # and legacy tools become collapsed groups in Streamlit's top nav. - "": [ - st.Page( - "pages/Blueprinting/overview.py", - title="分析总览", - icon="🧭", - url_path="blueprinting-overview", - default=True, - ), - ], - "Calculon 基线（旧版）": [ - st.Page( - "pages/LLM_Calc/overview.py", - title="Overview", - icon="📊", - url_path="calculon-overview", - ), - st.Page( - "pages/LLM_Calc/blockwise.py", - title="块粒度", - icon="🧱", - ), - st.Page( - "pages/LLM_Calc/distexp.py", - title="分布式实验", - icon="🔄", - ), - ], - }, - position="top", -) - - -# ============================================================================ -# 运行应用 -# ============================================================================ -pg.run() diff --git a/tests/validation/legacy/test_seqsel_fig1.py b/tests/validation/legacy/test_seqsel_fig1.py deleted file mode 100644 index d960e19..0000000 --- a/tests/validation/legacy/test_seqsel_fig1.py +++ /dev/null @@ -1,11 +0,0 @@ -from blueprinting.console import print_rich_table - - -def test_seqsel_fig1(): - from blueprinting.validation.legacy.seqsel_fig1 import seqsel_fig1 - - ret = seqsel_fig1() - print_rich_table(ret, caption="w+opt mem & act mem") - - assert ret["w+opt mem/rtol"].max() < 0.11 - assert ret["act mem/rtol"].max() < 0.1 diff --git a/tests/validation/legacy/test_seqsel_fig7.py b/tests/validation/legacy/test_seqsel_fig7.py deleted file mode 100644 index dbd7753..0000000 --- a/tests/validation/legacy/test_seqsel_fig7.py +++ /dev/null @@ -1,10 +0,0 @@ -from blueprinting.console import print_rich_table - - -def test_seqsel_fig7(): - from blueprinting.validation.legacy.seqsel_fig7 import seqsel_fig7 - - ret = seqsel_fig7() - print_rich_table(ret, caption="act mem") - - assert ret["act mem/rtol"].max() < 0.31 diff --git a/tests/validation/legacy/test_seqsel_tab5.py b/tests/validation/legacy/test_seqsel_tab5.py deleted file mode 100644 index 78019bd..0000000 --- a/tests/validation/legacy/test_seqsel_tab5.py +++ /dev/null @@ -1,10 +0,0 @@ -from blueprinting.console import print_rich_table - - -def test_seqsel_tab5(): - from blueprinting.validation.legacy.seqsel_tab5 import seqsel_tab5 - - ret = seqsel_tab5() - print_rich_table(ret, caption="iter time") - - assert ret["iter time/rtol"].max() < 0.09 diff --git a/tests/workbench/test_nicegui_workbench.py b/tests/workbench/test_nicegui_workbench.py index 393ccee..7cf3787 100644 --- a/tests/workbench/test_nicegui_workbench.py +++ b/tests/workbench/test_nicegui_workbench.py @@ -51,7 +51,6 @@ async def test_nicegui_workbench_loads_without_eager_analysis( await user.should_see("当前显示解析任务贡献，不是事件级 Timeline") await user.should_see(marker="run-analysis") await user.should_see(marker="sidebar-run-analysis") - await user.should_see("Calculon / Streamlit Legacy") await user.should_see(marker="mode-evidence") await user.should_see(marker="mode-float") diff --git a/tests/workbench/test_streamlit_workbench.py b/tests/workbench/test_streamlit_workbench.py deleted file mode 100644 index dfdac84..0000000 --- a/tests/workbench/test_streamlit_workbench.py +++ /dev/null @@ -1,35 +0,0 @@ -from __future__ import annotations - -from streamlit.testing.v1 import AppTest - - -def test_workbench_page_loads_without_eager_analysis() -> None: - app = AppTest.from_file("pages/Blueprinting/overview.py", default_timeout=30).run() - - assert not app.exception - assert [button.label for button in app.button] == ["运行 Blueprinting 分析"] - assert not app.metric - - -def test_overview_runs_blueprinting_analysis() -> None: - app = AppTest.from_file("pages/Blueprinting/overview.py", default_timeout=30).run() - - app.button[0].click().run() - - assert not app.exception - assert not app.error - assert app.success - assert {metric.label for metric in app.metric} == { - "迭代延迟", - "Token/s", - "Token/s/设备", - "单设备内存", - "主导项", - } - - -def test_navigation_defaults_to_blueprinting_without_path_collisions() -> None: - app = AppTest.from_file("streamlit_app.py", default_timeout=30).run() - - assert not app.exception - assert app.title[0].value == "🧭 分析总览" diff --git a/uv.lock b/uv.lock index c495006..bc1e5f5 100644 --- a/uv.lock +++ b/uv.lock @@ -176,36 +176,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" }, ] -[[package]] -name = "altair" -version = "6.0.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - 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