commit fabb9c34a63312d2c91eadcc717ae1bbcbb80075 Author: ModelHub XC Date: Mon Jun 8 16:07:13 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: OpenBMB/MiniCPM4-MCP Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/Modelfile b/Modelfile new file mode 100644 index 0000000..c93083a --- /dev/null +++ b/Modelfile @@ -0,0 +1,14 @@ +# ollama modelfile auto-generated by llamafactory + +FROM . + +TEMPLATE """{{ if .System }}<|im_start|>system +{{ .System }}<|im_end|> +{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user +{{ .Content }}<|im_end|> +<|im_start|>assistant +{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|> +{{ end }}{{ end }}""" + +PARAMETER stop "<|im_end|>" +PARAMETER num_ctx 4096 diff --git a/README.md b/README.md new file mode 100644 index 0000000..1c8d27c --- /dev/null +++ b/README.md @@ -0,0 +1,148 @@ +--- +license: apache-2.0 +language: +- zh +- en +pipeline_tag: text-generation +library_name: transformers +--- +
+ +
+ +

+GitHub Repo | +Technical Report +

+

+👋 Join us on Discord and WeChat +

+ +## What's New +- [2025.06.06] **MiniCPM4** series are released! This model achieves ultimate efficiency improvements while maintaining optimal performance at the same scale! It can achieve over 5x generation acceleration on typical end-side chips! You can find technical report [here](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf).🔥🔥🔥 + +## MiniCPM4 Series +MiniCPM4 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems. +- [MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B): The flagship of MiniCPM4, with 8B parameters, trained on 8T tokens. +- [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens. +- [MiniCPM4-8B-Eagle-FRSpec](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec): Eagle head for FRSpec, accelerating speculative inference for MiniCPM4-8B. +- [MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu): Eagle head trained with QAT for FRSpec, efficiently integrate speculation and quantization to achieve ultra acceleration for MiniCPM4-8B. +- [MiniCPM4-8B-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-vLLM): Eagle head in vLLM format, accelerating speculative inference for MiniCPM4-8B. +- [MiniCPM4-8B-marlin-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-marlin-Eagle-vLLM): Quantized Eagle head for vLLM format, accelerating speculative inference for MiniCPM4-8B. +- [BitCPM4-0.5B](https://huggingface.co/openbmb/BitCPM4-0.5B): Extreme ternary quantization applied to MiniCPM4-0.5B compresses model parameters into ternary values, achieving a 90% reduction in bit width. +- [BitCPM4-1B](https://huggingface.co/openbmb/BitCPM4-1B): Extreme ternary quantization applied to MiniCPM3-1B compresses model parameters into ternary values, achieving a 90% reduction in bit width. +- [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey): Based on MiniCPM4-8B, accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers. +- [MiniCPM4-MCP](https://huggingface.co/openbmb/MiniCPM4-MCP): Based on MiniCPM4-8B, accepts users' queries and available MCP tools as input and autonomously calls relevant MCP tools to satisfy users' requirements. (**<-- you are here**) + +## Introduction + +**MiniCPM4-MCP** is an open-source on-device LLM agent model jointly developed by [THUNLP](https://nlp.csai.tsinghua.edu.cn), Renmin University of China and [ModelBest](https://modelbest.cn/en), built on [MiniCPM-4](https://huggingface.co/openbmb/MiniCPM4-8B) with 8 billion parameters. It is capable of solving a wide range of real-world tasks by interacting with various tool and data resources through MCP. + +## Usage + +As of now, MiniCPM4-MCP supports the following: + +- Utilization of tools across 16 MCP servers: These servers span various categories, including office, lifestyle, communication, information, and work management. + +- Single-tool-calling capability: It can perform single- or multi-step tool calls using a single tool that complies with the MCP. + +- Cross-tool-calling capability: It can perform single- or multi-step tool calls using different tools that complies with the MCP. + + +## Inference + +### MCP Servers Deployment + +The MCP Servers supported by MiniCPM4-MCP include +[Airbnb](https://github.com/openbnb-org/mcp-server-airbnb), +[Amap-Maps](https://github.com/zxypro1/amap-maps-mcp-server), +[Arxiv-MCP-Server](https://github.com/blazickjp/arxiv-mcp-server), +[Calculator](https://github.com/githejie/mcp-server-calculator), +[Computer-Control-MCP](https://github.com/AB498/computer-control-mcp), +[Desktop-commander](https://github.com/wonderwhy-er/DesktopCommanderMCP), +[Filesystem](https://github.com/mark3labs/mcp-filesystem-server), +[Github](https://github.com/modelcontextprotocol/servers/tree/main/src/github), +[Gaode](https://github.com/perMAIN/gaode), +[MCP-Code-Executor](https://github.com/bazinga012/mcp_code_executor), +[MCP-DOCx](https://github.com/MeterLong/MCP-Doc), +[PPT](https://github.com/GongRzhe/Office-PowerPoint-MCP-Server), +[PPTx](https://github.com/supercurses/powerpoint), +[Simple-Time-Server](https://github.com/andybrandt/mcp-simple-timeserver), +[Slack](https://github.com/modelcontextprotocol/servers/tree/main/src/slack), and +[Whisper](https://github.com/arcaputo3/mcp-server-whisper). Follow the instructions provided in each server's repository for successful deployment. Note that not all tools in these servers will function properly in every environment. Some tools are unstable and may return errors such as timeouts or HTTP errors. During training data construction, tools with consistently high failure rates (e.g., those for which the LLM fails to produce a successful query even after hundreds of attempts) are filtered out. + +### MCP Client Setup + +We modified the existing MCP Client from the [mcp-cli](https://github.com/chrishayuk/mcp-cli) repository to enable interaction between MiniCPM and MCP Servers. +After the MCP Client performs a handshake with a Server, it retrieves a list of available tools. An example of tool information contained in this list is provided in [`available_tool_example.json`](https://github.com/OpenBMB/MiniCPM/blob/main/demo/minicpm4/MCP/available_tool_example.json). + +Once the available tools and user query are obtained, results can be generated using the following script logic: + +```bash +python generate_example.py \ +--tokenizer_path {path to MiniCPM4 tokenizer} \ +--base_url {vllm deployment URL} \ +--model {model name used in vllm deployment} \ +--output_path {path to save results} +``` +where the `generate_example.py` is located in [link](https://github.com/OpenBMB/MiniCPM/blob/main/demo/minicpm4/MCP/generate_example.py) and MiniCPM4 generates tool calls in the following format: + +``` + <|tool_call_start|> + ```python + read_file(path="/path/to/file") + ``` + <|tool_call_end|> +``` +You can build a custom parser for MiniCPM4 tool calls based on this format. The relevant parsing logic is located in `generate_example.py`. + +Since the [mcp-cli](https://github.com/chrishayuk/mcp-cli) repository supports the vLLM inference framework, MiniCPM4-MCP can also be integrated into `mcp-cli` by modifying vLLM accordingly. +Specifically, follow the instructions in [this link](https://github.com/OpenBMB/MiniCPM/tree/main/demo/minicpm3/function_call) to enable interaction between a client running the MiniCPM4-MCP model and the MCP Server. + + + + + +## Evaluation +The detailed evaluation script can be found on the [GitHub](https://github.com/OpenBMB/MiniCPM/tree/main/demo/minicpm4/MCP) page. The evaluation results are presented below. + +| MCP Server | | gpt-4o | | | qwen3 | | | minicpm4 | | +|-----------------------|----------------|--------------|--------------|---------------|--------------|--------------|----------------|--------------|--------------| +| | func | param | value | func | param | value | func | param | value | +| Airbnb | 89.3 | 67.9 | 53.6 | 92.8 | 60.7 | 50.0 | 96.4 | 67.9 | 50.0 | +| Amap-Maps | 79.8 | 77.5 | 50.0 | 74.4 | 72.0 | 41.0 | 89.3 | 85.7 | 39.9 | +| Arxiv-MCP-Server | 85.7 | 85.7 | 85.7 | 81.8 | 54.5 | 50.0 | 57.1 | 57.1 | 52.4 | +| Calculator | 100.0 | 100.0 | 20.0 | 80.0 | 80.0 | 13.3 | 100.0 | 100.0 | 6.67 | +| Computor-Control-MCP | 90.0 | 90.0 | 90.0 | 90.0 | 90.0 | 90.0 | 90.0 | 90.0 | 86.7 | +| Desktop-Commander | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | +| Filesystem | 63.5 | 63.5 | 31.3 | 69.7 | 69.7 | 26.0 | 83.3 | 83.3 | 42.7 | +|Github | 92.0 | 80.0 | 58.0 | 80.5 | 50.0 | 27.7 | 62.8 | 25.7 | 17.1 | +| Gaode | 71.1 | 55.6 | 17.8 | 68.8 | 46.6 | 24.4 | 68.9 | 46.7 | 15.6 | +| MCP-Code-Executor | 85.0 | 80.0 | 70.0 | 80.0 | 80.0 | 70.0 | 90.0 | 90.0 | 65.0 | +| MCP-Docx | 95.8 | 86.7 | 67.1 | 94.9 | 81.6 | 60.1 | 95.1 | 86.6 | 76.1 | +| PPT | 72.6 | 49.8 | 40.9 | 85.9 | 50.7 | 37.5 | 91.2 | 72.1 | 56.7 | +| PPTx | 64.2 | 53.7 | 13.4 | 91.0 | 68.6 | 20.9 | 91.0 | 58.2 | 26.9 | +| Simple-Time-Server | 90.0 | 70.0 | 70.0 | 90.0 | 90.0 | 90.0 | 90.0 | 60.0 | 60.0 | +| Slack | 100.0 | 90.0 | 70.0 | 100.0 | 100.0 | 65.0 | 100.0 | 100.0 | 100.0 | +| Whisper | 90.0 | 90.0 | 90.0 | 90.0 | 90.0 | 90.0 | 90.0 | 90.0 | 30.0 | +| **Average** | **80.2** | **70.2** | **49.1** | **83.5** | **67.7** | **43.8** | **88.3** | **76.1** | **51.2** | + +## Statement +- As a language model, MiniCPM generates content by learning from a vast amount of text. +- However, it does not possess the ability to comprehend or express personal opinions or value judgments. +- Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers. +- Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own. + +## LICENSE +- This repository and MiniCPM models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. + +## Citation +- Please cite our [paper](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf) if you find our work valuable. + +```bibtex +@article{minicpm4, + title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices}, + author={MiniCPM Team}, + year={2025} +} +``` diff --git a/added_tokens.json b/added_tokens.json new file mode 100644 index 0000000..7f0cedc --- /dev/null +++ b/added_tokens.json @@ -0,0 +1,10 @@ +{ + "<|execute_end|>": 73444, + "<|execute_start|>": 73443, + "<|fim_middle|>": 73446, + "<|fim_prefix|>": 73445, + "<|fim_suffix|>": 73447, + "<|im_end|>": 73440, + "<|im_start|>": 73441, + "<|tool_call|>": 73442 +} diff --git a/compressed_attention.py b/compressed_attention.py new file mode 100644 index 0000000..da35757 --- /dev/null +++ b/compressed_attention.py @@ -0,0 +1,1402 @@ +# coding=utf-8 +# Copyright 2025 The OpenBMB Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math +from typing import Any, Tuple, Union +from collections import Counter +import torch +import triton +import triton.language as tl +import warnings +from torch import nn +def is_hopper_gpu(): + if torch.cuda.is_available(): + device_capability = torch.cuda.get_device_capability() + major, minor = device_capability + return major == 9 + return False +def get_compressed_seqlens( + cu_seqlens: torch.Tensor, kernel_size: int, kernel_stride: int +): + # compute seqlens after compression + seqlens = cu_seqlens[1:] - cu_seqlens[:-1] + y_seqlens = torch.floor((seqlens - kernel_size) / kernel_stride).to(torch.int32) + 1 + # corner case, if sequence_length < kernel_size, no compression for this sequence + y_seqlens[seqlens < kernel_size] = 0 + y_cu_seqlens = torch.zeros( + y_seqlens.shape[0] + 1, dtype=torch.int32, device=cu_seqlens.device + ) + y_cu_seqlens[1:] = torch.cumsum(y_seqlens, dim=0) + return y_seqlens, y_cu_seqlens + + +def get_num_warps_stages(head_dim, block_size, is_hopper_gpu): + """ + Returns recommended num_warps and num_stages for a Sparse Attention kernel in Triton. + + Args: + head_dim (int): Size of the head dimension. + block_size (int): Size of the block in the attention matrix. + is_hopper_gpu (bool): True if Hopper GPU, False if Ampere GPU. + + Returns: + tuple: (num_warps, num_stages) recommended values. + """ + # Determine if head_dim and block_size exceed 64 + head_large = head_dim > 64 + block_large = block_size > 64 + + if is_hopper_gpu: + # Hopper GPU recommendations + if head_large and block_large: + num_warps = 8 + num_stages = 3 + elif head_large or block_large: + num_warps = 4 + num_stages = 3 + else: + num_warps = 2 + num_stages = 2 + else: + # Ampere GPU recommendations + if head_large and block_large: + num_warps = 8 + num_stages = 3 + elif head_large or block_large: + num_warps = 8 + num_stages = 3 + else: + num_warps = 2 + num_stages = 2 + return num_warps, num_stages + + +IS_HOPPER_GPU = is_hopper_gpu() + + +@triton.jit +def forward_kernel( + q_ptr, # Q: n x h x d + k_ptr, # K: n x h x d + v_ptr, # V: n x h x d + o_ptr, # O: n x h x d + lse_ptr, # LSE: h x n + # size and stride at compresstion + kernel_size, + kernel_stride, + # seqlens + cu_seqlens_q, + cu_seqlens_k, + # shape + NUM_KV_HEADS, + NUM_SHARE_Q_HEADS, + HEAD_DIM, + # sm_scale + sm_scale, + # stride + stride_qn, + stride_qh, + stride_qd, + stride_kn, + stride_kh, + stride_kd, + stride_vn, + stride_vh, + stride_vd, + stride_on, + stride_oh, + stride_od, + stride_lh, + stride_ln, + # META parameters + BLOCK_SIZE_Q: tl.constexpr, # q block size + BLOCK_SIZE_K: tl.constexpr, # k block size + BLOCK_SIZE_D: tl.constexpr, +): + qk_scale = sm_scale * 1.44269504 + # get batch id and head id + pid_b = tl.program_id(0) + pid_h = tl.program_id(1) + pid_q = tl.program_id(2) + pid_kh = pid_h // NUM_SHARE_Q_HEADS + # get q k start and len after rmpad + q_start = tl.load(cu_seqlens_q + pid_b) + q_len = tl.load(cu_seqlens_q + pid_b + 1) - q_start + k_start = tl.load(cu_seqlens_k + pid_b) + k_len = tl.load(cu_seqlens_k + pid_b + 1) - k_start + # skip first kernel_size query block, because they do no attend to any keys + q_start_in_seq = pid_q * BLOCK_SIZE_Q + kernel_size - 1 + if q_start_in_seq >= q_len: + return + # init qkv pointer + q_ptrs = tl.make_block_ptr( + base=q_ptr + q_start * stride_qn + pid_h * stride_qh, + shape=(q_len, HEAD_DIM), + strides=(stride_qn, stride_qd), + offsets=(q_start_in_seq, 0), + block_shape=(BLOCK_SIZE_Q, BLOCK_SIZE_D), + order=(1, 0), + ) + k_ptrs = tl.make_block_ptr( + base=k_ptr + k_start * stride_kn + pid_kh * stride_kh, + shape=(HEAD_DIM, k_len), + strides=(stride_kd, stride_kn), + offsets=(0, 0), + block_shape=(BLOCK_SIZE_D, BLOCK_SIZE_K), + order=(0, 1), + ) + v_ptrs = tl.make_block_ptr( + base=v_ptr + k_start * stride_vn + pid_kh * stride_vh, + shape=(k_len, HEAD_DIM), + strides=(stride_vn, stride_vd), + offsets=(0, 0), + block_shape=(BLOCK_SIZE_K, BLOCK_SIZE_D), + order=(1, 0), + ) + # load q + q = tl.load(q_ptrs, boundary_check=(0, 1), padding_option="zero") + # init statistics + off_q = tl.arange(0, BLOCK_SIZE_Q) + q_start_in_seq + off_k = tl.arange(0, BLOCK_SIZE_K) * kernel_stride + kernel_size - 1 + m_i = tl.full((BLOCK_SIZE_Q,), float("-inf"), dtype=tl.float32) + lse_i = tl.full((BLOCK_SIZE_Q,), float("-inf"), dtype=tl.float32) + acc_o = tl.full((BLOCK_SIZE_Q, BLOCK_SIZE_D), 0, dtype=tl.float32) + # attention + lo = 0 + hi = min(k_len, (q_start_in_seq + BLOCK_SIZE_Q - kernel_size) // kernel_stride + 1) + for i in range(lo, hi, BLOCK_SIZE_K): + i = tl.multiple_of(i, BLOCK_SIZE_K) + # load k + k = tl.load(k_ptrs, boundary_check=(1, 0), padding_option="zero") + # compute qk + qk = tl.zeros((BLOCK_SIZE_Q, BLOCK_SIZE_K), dtype=tl.float32) + qk += tl.where( + off_q[:, None] >= (i * kernel_stride + off_k)[None, :], 0, float("-inf") + ) + qk += tl.dot(q, k) * qk_scale + # compute m_ij and l_ij + m_ij = tl.maximum(m_i, tl.max(qk, axis=1)) + p = tl.exp2(qk - m_ij[:, None]) + l_ij = tl.sum(p, axis=1) + # scale acc_o + acc_o_scale = tl.exp2(m_i - m_ij) + acc_o = acc_o * acc_o_scale[:, None] + # load v and update acc_o + v = tl.load(v_ptrs, boundary_check=(0, 1), padding_option="zero") + p = p.to(v.dtype) + acc_o += tl.dot(p, v) + # update statistics + m_i = m_ij + lse_i = m_ij + tl.math.log2(tl.exp2(lse_i - m_ij) + l_ij) + # update ptrs + k_ptrs = tl.advance(k_ptrs, (0, BLOCK_SIZE_K)) + v_ptrs = tl.advance(v_ptrs, (BLOCK_SIZE_K, 0)) + # final scale + acc_o = acc_o * tl.exp2(m_i - lse_i)[:, None] + # save output + o_ptrs = tl.make_block_ptr( + base=o_ptr + q_start * stride_on + pid_h * stride_oh, + shape=(q_len, HEAD_DIM), + strides=(stride_on, stride_od), + offsets=(q_start_in_seq, 0), + block_shape=(BLOCK_SIZE_Q, BLOCK_SIZE_D), + order=(1, 0), + ) + tl.store(o_ptrs, acc_o.to(o_ptr.dtype.element_ty), boundary_check=(0, 1)) + # save lse + l_ptrs = lse_ptr + q_start * stride_ln + pid_h * stride_lh + off_q * stride_ln + tl.store(l_ptrs, lse_i, mask=off_q < q_len) + + +@triton.jit +def backward_sum_o_do( + o_ptr, # O: n x h x d + do_ptr, # dO: n x h x d + delta_ptr, # D: h x n + o_len, + HEAD_DIM, + stride_on, + stride_oh, + stride_od, + stride_don, + stride_doh, + stride_dod, + stride_dh, + stride_dn, + BLOCK_SIZE_O: tl.constexpr, + BLOCK_SIZE_D: tl.constexpr, +): + pid_n = tl.program_id(0) + pid_h = tl.program_id(1) + off_n = pid_n * BLOCK_SIZE_O + tl.arange(0, BLOCK_SIZE_O) + off_d = tl.arange(0, BLOCK_SIZE_D) + o = tl.load( + o_ptr + + off_n[:, None] * stride_on + + pid_h * stride_oh + + off_d[None, :] * stride_od, + mask=(off_n[:, None] < o_len) & (off_d[None, :] < HEAD_DIM), + other=0, + ).to(tl.float32) + do = tl.load( + do_ptr + + off_n[:, None] * stride_don + + pid_h * stride_doh + + off_d[None, :] * stride_dod, + mask=(off_n[:, None] < o_len) & (off_d[None, :] < HEAD_DIM), + other=0, + ).to(tl.float32) + delta = tl.sum(o * do, axis=1) + tl.store( + delta_ptr + pid_h * stride_dh + off_n * stride_dn, delta, mask=off_n < o_len + ) + + +@triton.jit +def backward_dkdv( + q_ptr, # Q: n x qh x d + k_ptr, # K: n x kh x d + v_ptr, # V: n x kh x d + lse_ptr, # LSE: qh x n + d_ptr, # Delta: qh x n + do_ptr, + dk_ptr, # DK: sh x n x kh x d + dv_ptr, # DV: sh x n x kh x d + kernel_size, + kernel_stride, + # seqlens + cu_seqlens_q, + cu_seqlens_k, + # shape + NUM_KV_HEADS, + NUM_SHARE_Q_HEADS, + HEAD_DIM, + # sm_scale + sm_scale, + # stride + stride_qn, + stride_qh, + stride_qd, + stride_kn, + stride_kh, + stride_kd, + stride_vn, + stride_vh, + stride_vd, + stride_lh, + stride_ln, + stride_dh, + stride_dn, + stride_don, + stride_doh, + stride_dod, + stride_dks, + stride_dkn, + stride_dkh, + stride_dkd, + stride_dvs, + stride_dvn, + stride_dvh, + stride_dvd, + # META parameters + BLOCK_SIZE_Q: tl.constexpr, # q block size + BLOCK_SIZE_K: tl.constexpr, # k block size + BLOCK_SIZE_D: tl.constexpr, +): + qk_scale = sm_scale * 1.44269504 + # get batch id and head id + pid_b = tl.program_id(0) + pid_h = tl.program_id(1) + pid_kh = pid_h // NUM_SHARE_Q_HEADS + pid_sh = pid_h % NUM_SHARE_Q_HEADS + pid_k = tl.program_id(2) + # get q k start and len after rmpad + q_start = tl.load(cu_seqlens_q + pid_b) + q_len = tl.load(cu_seqlens_q + pid_b + 1) - q_start + k_start = tl.load(cu_seqlens_k + pid_b) + k_len = tl.load(cu_seqlens_k + pid_b + 1) - k_start + if BLOCK_SIZE_K * pid_k >= k_len: + return + # init pointers + k_ptrs = tl.make_block_ptr( + base=k_ptr + k_start * stride_kn + pid_kh * stride_kh, + shape=(k_len, HEAD_DIM), + strides=(stride_kn, stride_kd), + offsets=(pid_k * BLOCK_SIZE_K, 0), + block_shape=(BLOCK_SIZE_K, BLOCK_SIZE_D), + order=(1, 0), + ) + dk_ptrs = tl.make_block_ptr( + base=dk_ptr + k_start * stride_dkn + pid_kh * stride_dkh + pid_sh * stride_dks, + shape=(k_len, HEAD_DIM), + strides=(stride_dkn, stride_dkd), + offsets=(pid_k * BLOCK_SIZE_K, 0), + block_shape=(BLOCK_SIZE_K, BLOCK_SIZE_D), + order=(1, 0), + ) + v_ptrs = tl.make_block_ptr( + base=v_ptr + k_start * stride_vn + pid_kh * stride_vh, + shape=(k_len, HEAD_DIM), + strides=(stride_vn, stride_vd), + offsets=(pid_k * BLOCK_SIZE_K, 0), + block_shape=(BLOCK_SIZE_K, BLOCK_SIZE_D), + order=(1, 0), + ) + dv_ptrs = tl.make_block_ptr( + base=dv_ptr + k_start * stride_dvn + pid_kh * stride_dvh + pid_sh * stride_dvs, + shape=(k_len, HEAD_DIM), + strides=(stride_dvn, stride_dvd), + offsets=(pid_k * BLOCK_SIZE_K, 0), + block_shape=(BLOCK_SIZE_K, BLOCK_SIZE_D), + order=(1, 0), + ) + # offsets + off_q = tl.arange(0, BLOCK_SIZE_Q) + off_k = ( + pid_k * BLOCK_SIZE_K * kernel_stride + + tl.arange(0, BLOCK_SIZE_K) * kernel_stride + + kernel_size + - 1 + ) + # load k v and keep in SRAM + k = tl.load(k_ptrs, boundary_check=(0, 1), padding_option="zero") + v = tl.load(v_ptrs, boundary_check=(0, 1), padding_option="zero") + # init dk dv + dk = tl.zeros((BLOCK_SIZE_K, BLOCK_SIZE_D), dtype=tl.float32) + dv = tl.zeros((BLOCK_SIZE_K, BLOCK_SIZE_D), dtype=tl.float32) + q_lo = pid_k * BLOCK_SIZE_K * kernel_stride + kernel_size - 1 + q_ptrs = tl.make_block_ptr( + base=q_ptr + q_start * stride_qn + pid_h * stride_qh, + shape=(HEAD_DIM, q_len), + strides=(stride_qd, stride_qn), + offsets=(0, q_lo), + block_shape=(BLOCK_SIZE_D, BLOCK_SIZE_Q), + order=(0, 1), + ) + do_ptrs = tl.make_block_ptr( + base=do_ptr + q_start * stride_don + pid_h * stride_doh, + shape=(HEAD_DIM, q_len), + strides=(stride_dod, stride_don), + offsets=(0, q_lo), + block_shape=(BLOCK_SIZE_D, BLOCK_SIZE_Q), + order=(0, 1), + ) + d_ptrs = tl.make_block_ptr( + base=d_ptr + q_start * stride_dn + pid_h * stride_dh, + shape=(1, q_len), + strides=(0, stride_dn), + offsets=(0, q_lo), + block_shape=(1, BLOCK_SIZE_Q), + order=(1, 0), + ) + lse_ptrs = tl.make_block_ptr( + base=lse_ptr + q_start * stride_ln + pid_h * stride_lh, + shape=(1, q_len), + strides=(0, stride_ln), + offsets=(0, q_lo), + block_shape=(1, BLOCK_SIZE_Q), + order=(0, 1), + ) + # loop for q blocks + for i in range(q_lo, q_len, BLOCK_SIZE_Q): + # load + q = tl.load(q_ptrs, boundary_check=(0, 1), padding_option="zero") + do = tl.load(do_ptrs, boundary_check=(0, 1), padding_option="zero") + lse = tl.load(lse_ptrs, boundary_check=(0, 1), padding_option="zero") + d = tl.load(d_ptrs, boundary_check=(0, 1), padding_option="zero") + # compute qk + # [BLOCK_SIZE_K, HEAD_DIM] @ [HEAD_DIM, BLOCK_SIE_Q] -> [BLOCK_SIZE_K, BLOCK_SIE_Q] + qk = tl.where(off_k[:, None] <= (off_q + i)[None, :], float(0.0), float("-inf")) + qk += tl.dot(k, q) * qk_scale + # compute p, ds + # [BLOCK_SIZE_K, BLOCK_SIE_Q] - [1, BLOCK_SIZE_Q] -> [BLOCK_SIZE_K, BLOCK_SIE_Q] + p = tl.exp2(qk - lse) + # [BLOCK_SIZE_K, HEAD_DIM] @ [HEAD_DIM, BLOCK_SIE_Q] -> [BLOCK_SIZE_K, BLOCK_SIE_Q] + dp = tl.dot(v, do) + ds = sm_scale * p * (dp - d) + # cast dtype + p = p.to(do.dtype) + ds = ds.to(q.dtype) + # update dk and dv + # [BLOCK_SIZE_K, BLOCK_SIE_Q] @ [BLOCK_SIE_Q, HEAD_DIM] -> [BLOCK_SIZE_K, HEAD_DIM] + dk += tl.dot(ds, tl.trans(q)) + dv += tl.dot(p, tl.trans(do)) + # increment pointers + q_ptrs = tl.advance(q_ptrs, (0, BLOCK_SIZE_Q)) + do_ptrs = tl.advance(do_ptrs, (0, BLOCK_SIZE_Q)) + lse_ptrs = tl.advance(lse_ptrs, (0, BLOCK_SIZE_Q)) + d_ptrs = tl.advance(d_ptrs, (0, BLOCK_SIZE_Q)) + # save dk dv + tl.store(dk_ptrs, dk.to(dk_ptr.dtype.element_ty), boundary_check=(0, 1)) + tl.store(dv_ptrs, dv.to(dv_ptr.dtype.element_ty), boundary_check=(0, 1)) + + +@triton.jit +def backward_dq( + q_ptr, # Q: n x qh x d + k_ptr, # K: n x kh x d + v_ptr, # V: n x kh x d + lse_ptr, # LSE: qh x n + d_ptr, # Delta: qh x n + do_ptr, + dq_ptr, + kernel_size, + kernel_stride, + # seqlens + cu_seqlens_q, + cu_seqlens_k, + # shape + NUM_KV_HEADS, + NUM_SHARE_Q_HEADS, + HEAD_DIM, + # sm_scale + sm_scale, + # stride + stride_qn, + stride_qh, + stride_qd, + stride_kn, + stride_kh, + stride_kd, + stride_vn, + stride_vh, + stride_vd, + stride_lh, + stride_ln, + stride_dh, + stride_dn, + stride_don, + stride_doh, + stride_dod, + stride_dqn, + stride_dqh, + stride_dqd, + # META parameters + BLOCK_SIZE_Q: tl.constexpr, # q block size + BLOCK_SIZE_K: tl.constexpr, # k block size + BLOCK_SIZE_D: tl.constexpr, +): + qk_scale = sm_scale * 1.44269504 + # get batch id and head id + pid_b = tl.program_id(0) + pid_h = tl.program_id(1) + pid_q = tl.program_id(2) + pid_kh = pid_h // NUM_SHARE_Q_HEADS + # get q k start and len after rmpad + q_start = tl.load(cu_seqlens_q + pid_b) + q_len = tl.load(cu_seqlens_q + pid_b + 1) - q_start + k_start = tl.load(cu_seqlens_k + pid_b) + k_len = tl.load(cu_seqlens_k + pid_b + 1) - k_start + # skip first kernel_size query block, because they do no attend to any keys + q_start_in_seq = pid_q * BLOCK_SIZE_Q + kernel_size - 1 + if q_start_in_seq >= q_len: + return + # init pointers + q_ptrs = tl.make_block_ptr( + base=q_ptr + q_start * stride_qn + pid_h * stride_qh, + shape=(q_len, HEAD_DIM), + strides=(stride_qn, stride_qd), + offsets=(q_start_in_seq, 0), + block_shape=(BLOCK_SIZE_Q, BLOCK_SIZE_D), + order=(1, 0), + ) + dq_ptrs = tl.make_block_ptr( + base=dq_ptr + q_start * stride_dqn + pid_h * stride_dqh, + shape=(q_len, HEAD_DIM), + strides=(stride_dqn, stride_dqd), + offsets=(q_start_in_seq, 0), + block_shape=(BLOCK_SIZE_Q, BLOCK_SIZE_D), + order=(1, 0), + ) + k_ptrs = tl.make_block_ptr( + base=k_ptr + k_start * stride_kn + pid_kh * stride_kh, + shape=(k_len, HEAD_DIM), + strides=(stride_kn, stride_kd), + offsets=(0, 0), + block_shape=(BLOCK_SIZE_K, BLOCK_SIZE_D), + order=(1, 0), + ) + v_ptrs = tl.make_block_ptr( + base=v_ptr + k_start * stride_vn + pid_kh * stride_vh, + shape=(HEAD_DIM, k_len), + strides=(stride_vd, stride_vn), + offsets=(0, 0), + block_shape=(BLOCK_SIZE_D, BLOCK_SIZE_K), + order=(0, 1), + ) + do_ptrs = tl.make_block_ptr( + base=do_ptr + q_start * stride_don + pid_h * stride_doh, + shape=(q_len, HEAD_DIM), + strides=(stride_don, stride_dod), + offsets=(q_start_in_seq, 0), + block_shape=(BLOCK_SIZE_Q, BLOCK_SIZE_D), + order=(1, 0), + ) + d_ptrs = tl.make_block_ptr( + base=d_ptr + q_start * stride_dn + pid_h * stride_dh, + shape=(q_len, 1), + strides=(stride_dn, stride_dh), + offsets=(q_start_in_seq, 0), + block_shape=(BLOCK_SIZE_Q, 1), + order=(0, 1), + ) + lse_ptrs = tl.make_block_ptr( + base=lse_ptr + q_start * stride_ln + pid_h * stride_lh, + shape=(q_len, 1), + strides=(stride_ln, stride_lh), + offsets=(q_start_in_seq, 0), + block_shape=(BLOCK_SIZE_Q, 1), + order=(0, 1), + ) + # offsets + off_q = tl.arange(0, BLOCK_SIZE_Q) + q_start_in_seq + off_k = tl.arange(0, BLOCK_SIZE_K) * kernel_stride + kernel_size - 1 + # load q, do, lse, delta, and keep in SRAM + q = tl.load(q_ptrs, boundary_check=(1, 0), padding_option="zero") + do = tl.load(do_ptrs, boundary_check=(0, 1), padding_option="zero") + lse = tl.load(lse_ptrs, boundary_check=(0, 1), padding_option="zero") + d = tl.load(d_ptrs, boundary_check=(0, 1), padding_option="zero") + # init dq + dq = tl.zeros((BLOCK_SIZE_Q, BLOCK_SIZE_D), dtype=tl.float32) + lo = 0 + hi = min(k_len, (q_start_in_seq + BLOCK_SIZE_Q - kernel_size) // kernel_stride + 1) + for i in range(lo, hi, BLOCK_SIZE_K): + # load + k = tl.load(k_ptrs, boundary_check=(0, 1), padding_option="zero") + v = tl.load(v_ptrs, boundary_check=(0, 1), padding_option="zero") + # compute qk + qk = tl.zeros((BLOCK_SIZE_Q, BLOCK_SIZE_K), dtype=tl.float32) + qk += tl.where( + off_q[:, None] >= (i * kernel_stride + off_k)[None, :], 0, float("-inf") + ) + qk += tl.dot(q, tl.trans(k)) * qk_scale + # compute p, ds + p = tl.exp2(qk - lse) + dp = tl.dot(do, v) + ds = sm_scale * p * (dp - d) + # cast dtype + ds = ds.to(q.dtype) + # update dq + dq += tl.dot(ds, k) + # increment pointers + k_ptrs = tl.advance(k_ptrs, (BLOCK_SIZE_K, 0)) + v_ptrs = tl.advance(v_ptrs, (0, BLOCK_SIZE_K)) + # save dq + tl.store(dq_ptrs, dq.to(dq_ptr.dtype.element_ty), boundary_check=(0, 1)) + + +def _compressed_attention_fwd( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + kernel_size: int, + kernel_stride: int, + cu_seqlens_q: torch.Tensor, + cu_seqlens_k: torch.Tensor, + max_seqlen_q: torch.Tensor, + max_seqlen_k: torch.Tensor, + sm_scale: float, +): + # dtype check + assert k.dtype == q.dtype and v.dtype == q.dtype + assert cu_seqlens_q.dtype == torch.int32 and cu_seqlens_k.dtype == torch.int32 + # shape + q_len, num_q_heads, head_dim = q.shape + k_len, num_k_heads, head_dim = k.shape + v_len, num_v_heads, head_dim = v.shape + batch_size = cu_seqlens_q.shape[0] - 1 + assert k_len == v_len and q_len > k_len + # gqa + assert num_k_heads == num_v_heads + assert num_q_heads % num_k_heads == 0 + num_share_q_heads = num_q_heads // num_k_heads + # output tensor + o = torch.zeros_like(q) + lse = torch.full( + (num_q_heads, q_len), + fill_value=-torch.inf, + dtype=torch.float32, + device=q.device, + ) + # launch kernel + grid = lambda META: ( + batch_size, + num_q_heads, + triton.cdiv(max_seqlen_q, META["BLOCK_SIZE_Q"]), + ) + BLOCK_SIZE_Q = 128 + BLOCK_SIZE_K = 128 + BLOCK_SIZE_D = triton.next_power_of_2(head_dim) + num_warps, num_stages = get_num_warps_stages(head_dim, BLOCK_SIZE_Q, IS_HOPPER_GPU) + forward_kernel[grid]( + q, + k, + v, + o, + lse, + kernel_size, + kernel_stride, + cu_seqlens_q, + cu_seqlens_k, + num_k_heads, + num_share_q_heads, + head_dim, + sm_scale, + q.stride(0), + q.stride(1), + q.stride(2), + k.stride(0), + k.stride(1), + k.stride(2), + v.stride(0), + v.stride(1), + v.stride(2), + o.stride(0), + o.stride(1), + o.stride(2), + lse.stride(0), + lse.stride(1), + BLOCK_SIZE_Q=BLOCK_SIZE_Q, + BLOCK_SIZE_K=BLOCK_SIZE_K, + BLOCK_SIZE_D=BLOCK_SIZE_D, + num_warps=num_warps, + num_stages=num_stages, + ) + return o, lse + + +def _compressed_attention_bwd( + o: torch.Tensor, + do: torch.Tensor, + lse: torch.Tensor, + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + kernel_size: int, + kernel_stride: int, + cu_seqlens_q: torch.Tensor, + cu_seqlens_k: torch.Tensor, + max_seqlen_q: torch.Tensor, + max_seqlen_k: torch.Tensor, + sm_scale: float, +): + q_len, num_q_heads, head_dim = q.shape + k_len, num_k_heads, head_dim = k.shape + v_len, num_v_heads, head_dim = v.shape + o_len, num_o_heads, head_dim = o.shape + num_share_q_heads = num_q_heads // num_k_heads + # compute D + delta = torch.zeros([num_o_heads, o_len], device=o.device, dtype=torch.float32) + grid = lambda META: (triton.cdiv(o_len, META["BLOCK_SIZE_O"]), num_o_heads) + BLOCK_SIZE_O = 256 + BLOCK_SIZE_D = triton.next_power_of_2(head_dim) + num_warps, num_stages = get_num_warps_stages(head_dim, BLOCK_SIZE_O, IS_HOPPER_GPU) + backward_sum_o_do[grid]( + o, + do, + delta, + o_len, + head_dim, + o.stride(0), + o.stride(1), + o.stride(2), + do.stride(0), + do.stride(1), + do.stride(2), + delta.stride(0), + delta.stride(1), + BLOCK_SIZE_O=BLOCK_SIZE_O, + BLOCK_SIZE_D=BLOCK_SIZE_D, + num_warps=num_warps, + num_stages=num_stages, + ) + # compute dk dv + dk = torch.zeros( + num_share_q_heads, k_len, num_k_heads, head_dim, device=k.device, dtype=k.dtype + ) + dv = torch.zeros( + num_share_q_heads, k_len, num_k_heads, head_dim, device=k.device, dtype=k.dtype + ) + batch_size = cu_seqlens_q.shape[0] - 1 + grid = lambda META: ( + batch_size, + num_q_heads, + triton.cdiv(max_seqlen_k, META["BLOCK_SIZE_K"]), + ) + BLOCK_SIZE_Q = 64 + BLOCK_SIZE_K = 128 + BLOCK_SIZE_D = triton.next_power_of_2(head_dim) + num_warps, num_stages = get_num_warps_stages(head_dim, BLOCK_SIZE_K, IS_HOPPER_GPU) + backward_dkdv[grid]( + q, + k, + v, + lse, + delta, + do, + dk, + dv, + kernel_size, + kernel_stride, + cu_seqlens_q, + cu_seqlens_k, + num_k_heads, + num_share_q_heads, + head_dim, + sm_scale, + q.stride(0), + q.stride(1), + q.stride(2), + k.stride(0), + k.stride(1), + k.stride(2), + v.stride(0), + v.stride(1), + v.stride(2), + lse.stride(0), + lse.stride(1), + delta.stride(0), + delta.stride(1), + do.stride(0), + do.stride(1), + do.stride(2), + dk.stride(0), + dk.stride(1), + dk.stride(2), + dk.stride(3), + dv.stride(0), + dv.stride(1), + dv.stride(2), + dv.stride(3), + BLOCK_SIZE_Q=BLOCK_SIZE_Q, + BLOCK_SIZE_K=BLOCK_SIZE_K, + BLOCK_SIZE_D=BLOCK_SIZE_D, + num_warps=num_warps, + num_stages=num_stages, + ) + dk = dk.sum(0) + dv = dv.sum(0) + # compute dq + dq = torch.zeros_like(q) + grid = lambda META: ( + batch_size, + num_q_heads, + triton.cdiv(max_seqlen_q, META["BLOCK_SIZE_Q"]), + ) + BLOCK_SIZE_Q = 128 + BLOCK_SIZE_K = 64 + num_warps, num_stages = get_num_warps_stages(head_dim, BLOCK_SIZE_Q, IS_HOPPER_GPU) + backward_dq[grid]( + q, + k, + v, + lse, + delta, + do, + dq, + kernel_size, + kernel_stride, + cu_seqlens_q, + cu_seqlens_k, + num_k_heads, + num_share_q_heads, + head_dim, + sm_scale, + q.stride(0), + q.stride(1), + q.stride(2), + k.stride(0), + k.stride(1), + k.stride(2), + v.stride(0), + v.stride(1), + v.stride(2), + lse.stride(0), + lse.stride(1), + delta.stride(0), + delta.stride(1), + do.stride(0), + do.stride(1), + do.stride(2), + dq.stride(0), + dq.stride(1), + dq.stride(2), + BLOCK_SIZE_Q=BLOCK_SIZE_Q, + BLOCK_SIZE_K=BLOCK_SIZE_K, + BLOCK_SIZE_D=BLOCK_SIZE_D, + num_warps=num_warps, + num_stages=num_stages, + ) + return dq, dk, dv + + +class CompressedAttention(torch.autograd.Function): + @staticmethod + def forward( + ctx, + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + kernel_size: int, + kernel_stride: int, + cu_seqlens_q: torch.Tensor, + cu_seqlens_k: torch.Tensor, + max_seqlen_q: torch.Tensor, + max_seqlen_k: torch.Tensor, + sm_scale=None, + ): + # dtype check + assert q.dtype == torch.bfloat16 or q.dtype == torch.float16 + assert q.dtype == k.dtype and k.dtype == v.dtype + assert cu_seqlens_q.dtype == torch.int32 and cu_seqlens_k.dtype == torch.int32 + # softmax scale + if sm_scale is None: + sm_scale = 1 / math.sqrt(q.shape[-1]) + o, lse = _compressed_attention_fwd( + q, + k, + v, + kernel_size, + kernel_stride, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_q, + max_seqlen_k, + sm_scale, + ) + ctx.save_for_backward(q, k, v, o, lse, cu_seqlens_q, cu_seqlens_k) + ctx.sm_scale = sm_scale + ctx.max_seqlen_q = max_seqlen_q + ctx.max_seqlen_k = max_seqlen_k + ctx.kernel_size = kernel_size + ctx.kernel_stride = kernel_stride + return o, lse + + @staticmethod + def backward(ctx, do: torch.Tensor, *args) -> Any: + q, k, v, o, lse, cu_seqlens_q, cu_seqlens_k = ctx.saved_tensors + max_seqlen_q = ctx.max_seqlen_q + max_seqlen_k = ctx.max_seqlen_k + sm_scale = ctx.sm_scale + kernel_size = ctx.kernel_size + kernel_stride = ctx.kernel_stride + dq, dk, dv = _compressed_attention_bwd( + o, + do, + lse, + q, + k, + v, + kernel_size, + kernel_stride, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_q, + max_seqlen_k, + sm_scale, + ) + return dq, dk, dv, None, None, None, None, None, None, None + + +@triton.jit +def score_kernel( + q_ptr, + k_ptr, + lse_ptr, + s_ptr, + kernel_size, + kernel_stride, + # seqlens + cu_seqlens_q, + cu_seqlens_k, + # shape + NUM_KV_HEADS, + NUM_SHARE_Q_HEADS, + HEAD_DIM, + # sm_scale + sm_scale, + # stride + stride_qn, + stride_qh, + stride_qd, + stride_kn, + stride_kh, + stride_kd, + stride_lh, + stride_ln, + stride_sh, + stride_sq, + stride_sk, + # META parameters + BLOCK_SIZE_Q: tl.constexpr, # q block size + BLOCK_SIZE_K: tl.constexpr, # k block size + BLOCK_SIZE_D: tl.constexpr, +): + qk_scale = sm_scale * 1.44269504 + # get batch id and head id + pid_bkh = tl.program_id(0) + pid_b = pid_bkh // NUM_KV_HEADS + pid_kh = pid_bkh % NUM_KV_HEADS + pid_q = tl.program_id(1) + pid_k = tl.program_id(2) + # get q k start and len after rmpad + q_start = tl.load(cu_seqlens_q + pid_b) + q_len = tl.load(cu_seqlens_q + pid_b + 1) - q_start + k_start = tl.load(cu_seqlens_k + pid_b) + k_len = tl.load(cu_seqlens_k + pid_b + 1) - k_start + if pid_q * BLOCK_SIZE_Q >= q_len or pid_k * BLOCK_SIZE_K >= k_len: + return + # init k pointer and load k + k_ptrs = tl.make_block_ptr( + base=k_ptr + k_start * stride_kn + pid_kh * stride_kh, + shape=(HEAD_DIM, k_len), + strides=(stride_kd, stride_kn), + offsets=(0, pid_k * BLOCK_SIZE_K), + block_shape=(BLOCK_SIZE_D, BLOCK_SIZE_K), + order=(0, 1), + ) + k = tl.load(k_ptrs, boundary_check=(0, 1), padding_option="zero") + # offsets + off_q = tl.arange(0, BLOCK_SIZE_Q) + pid_q * BLOCK_SIZE_Q + off_k = tl.arange(0, BLOCK_SIZE_K) + pid_k * BLOCK_SIZE_K + causal_mask = off_q[:, None] >= (off_k * kernel_stride + kernel_size - 1)[None, :] + # init score + s = tl.zeros((BLOCK_SIZE_Q, BLOCK_SIZE_K), dtype=tl.float32) + # loop over gqa heads + for h in range(NUM_SHARE_Q_HEADS): + pid_h = pid_kh * NUM_SHARE_Q_HEADS + h + q_ptrs = tl.make_block_ptr( + base=q_ptr + q_start * stride_qn + pid_h * stride_qh, + shape=(q_len, HEAD_DIM), + strides=(stride_qn, stride_qd), + offsets=(pid_q * BLOCK_SIZE_Q, 0), + block_shape=(BLOCK_SIZE_Q, BLOCK_SIZE_D), + order=(1, 0), + ) + lse_ptrs = tl.make_block_ptr( + base=lse_ptr + q_start * stride_ln + pid_h * stride_lh, + shape=(q_len, 1), + strides=(stride_ln, stride_lh), + offsets=(pid_q * BLOCK_SIZE_Q, 0), + block_shape=(BLOCK_SIZE_Q, 1), + order=(0, 1), + ) + # load q and lse + q = tl.load(q_ptrs, boundary_check=(0, 1), padding_option="zero") + lse = tl.load(lse_ptrs, boundary_check=(0, 1), padding_option="zero") + # compute qk + qk = tl.zeros((BLOCK_SIZE_Q, BLOCK_SIZE_K), dtype=tl.float32) + qk += tl.dot(q, k) * qk_scale + # compute score + s += tl.where(causal_mask, tl.exp2(qk - lse), 0) + # save output + s_ptrs = tl.make_block_ptr( + base=s_ptr + pid_kh * stride_sh + q_start * stride_sq, + shape=(q_len, k_len), + strides=(stride_sq, stride_sk), + offsets=(pid_q * BLOCK_SIZE_Q, pid_k * BLOCK_SIZE_K), + block_shape=(BLOCK_SIZE_Q, BLOCK_SIZE_K), + order=(1, 0), + ) + tl.store(s_ptrs, s.to(s_ptr.dtype.element_ty), boundary_check=(0, 1)) + + +def _get_attention_score( + q: torch.Tensor, # [total_query_len, num_q_heads, head_dim] + k: torch.Tensor, # [total_key_len, num_k_heads, head_dim] + lse: torch.Tensor, # [num_q_heads, total_query_len] + kernel_size: int, + kernel_stride: int, + cu_seqlens_q: torch.Tensor, + cu_seqlens_k: torch.Tensor, + max_seqlen_q: int, + max_seqlen_k: int, + sm_scale: float, +) -> torch.Tensor: + # dtype check + assert q.dtype == torch.bfloat16 or q.dtype == torch.float16 + assert q.dtype == k.dtype + assert cu_seqlens_q.dtype == torch.int32 and cu_seqlens_k.dtype == torch.int32 + assert ( + lse.dtype == torch.float32 + ) # lse here is log2(sum(exp(qk*scale))), not log(sum(exp(qk*scale))) + # shape + q_len, num_q_heads, head_dim = q.shape + k_len, num_k_heads, head_dim = k.shape + batch_size = cu_seqlens_q.shape[0] - 1 + assert q_len > k_len + if sm_scale is None: + sm_scale = 1 / math.sqrt(head_dim) + # gqa + assert num_q_heads % num_k_heads == 0 + num_share_q_heads = num_q_heads // num_k_heads + # init score + score = torch.zeros( + num_k_heads, q_len, max_seqlen_k, dtype=torch.float32, device=q.device + ) + # launch kernel + grid = lambda META: ( + batch_size * num_k_heads, + triton.cdiv(max_seqlen_q, META["BLOCK_SIZE_Q"]), + triton.cdiv(max_seqlen_k, META["BLOCK_SIZE_K"]), + ) + BLOCK_SIZE_Q = 128 + BLOCK_SIZE_K = 128 + BLOCK_SIZE_D = triton.next_power_of_2(head_dim) + score_kernel[grid]( + q, + k, + lse, + score, + kernel_size, + kernel_stride, + cu_seqlens_q, + cu_seqlens_k, + num_k_heads, + num_share_q_heads, + head_dim, + sm_scale, + q.stride(0), + q.stride(1), + q.stride(2), + k.stride(0), + k.stride(1), + k.stride(2), + lse.stride(0), + lse.stride(1), + score.stride(0), + score.stride(1), + score.stride(2), + BLOCK_SIZE_Q=BLOCK_SIZE_Q, + BLOCK_SIZE_K=BLOCK_SIZE_K, + BLOCK_SIZE_D=BLOCK_SIZE_D, + num_warps=8, + num_stages=3, + ) + return score + + +@triton.jit +def _transform_score_kernel( + s_ptr, # score, shape: [num_heads, q_len, k_len] + bs_ptr, # block wise score: [num_heads, q_len, num_k_block] + offs, + cu_seqlens_q, + # shape + num_heads, + num_offs, + max_k_len, + max_blocks, + pad_len, + # kernel & block size + block_size, + block_stride, # block_size // kernel_stride + init_blocks, + local_blocks, + # stride + stride_sh, + stride_sq, + stride_sk, + stride_bsh, + stride_bsq, + stride_bsk, + BLOCK_SIZE_Q: tl.constexpr, + BLOCK_SIZE_K: tl.constexpr, + BLOCK_SIZE_O: tl.constexpr, +): + pid_bh = tl.program_id(0) + pid_b = pid_bh // num_heads + pid_h = pid_bh % num_heads + pid_q = tl.program_id(1) + pid_k = tl.program_id(2) + q_start = tl.load(cu_seqlens_q + pid_b) + q_len = tl.load(cu_seqlens_q + pid_b + 1) - q_start + k_start = pid_k * BLOCK_SIZE_K + if pid_q * BLOCK_SIZE_Q >= q_len: + return + # load weight + off_o = tl.arange(0, BLOCK_SIZE_O) + w = tl.load(offs + off_o, mask=off_o < num_offs, other=0) + # load score + off_q = pid_q * BLOCK_SIZE_Q + tl.arange(0, BLOCK_SIZE_Q) + off_k = (k_start + tl.arange(0, BLOCK_SIZE_K)) * block_stride - pad_len + off_k = off_k[None, :] + off_o[:, None] + s_ptrs = ( + s_ptr + + q_start * stride_sq + + pid_h * stride_sh + + off_q[:, None, None] * stride_sq + + off_k[None, :, :] * stride_sk + ) + # weighted sum, [BQ, BO, BK] * [1, BO, 1] -> [BQ, BO, BK] -> [BQ, BK] + s = tl.load( + s_ptrs, + mask=(off_q < q_len)[:, None, None] & (off_k >= 0) & (off_k < max_k_len), + other=0, + ) + s = s * w[None, :, None] + s = tl.max(s, axis=1) + # init mask and local mask + off_bq = off_q // block_size + off_bk = tl.arange(0, BLOCK_SIZE_K) + + s = tl.where( + # For local blocks: set to negative infinity (exclude from topk) + (off_bq[:, None] >= (off_bk + k_start)[None, :]) & (off_bq[:, None] < (off_bk + k_start)[None, :] + local_blocks), + float("-inf"), + s, + ) + + # Keep the original conditions for init_blocks and query location as infinity + s = tl.where( + (off_bk[None, :] < init_blocks - k_start) + # Force blocks where the query is located to have infinite score (always include in topk) + | (off_bq[:, None] == (off_bk + k_start)[None, :]), + float("inf"), + s, + ) + # store block wise score + bs_ptrs = ( + bs_ptr + + q_start * stride_bsq + + k_start * stride_bsk + + pid_h * stride_bsh + + off_q[:, None] * stride_bsq + + off_bk[None, :] * stride_bsk + ) + tl.store( + bs_ptrs, + s, + mask=(off_q < q_len)[:, None] & (off_bk < max_blocks - k_start)[None, :], + ) + + +def transform_score( + score: torch.Tensor, + kernel_size: int, + kernel_stride: int, + block_size: int, + cu_seqlens_q: torch.Tensor, + cu_seqlens_k: torch.Tensor, + max_seqlen_q: int, + max_seqlen_k: int, + init_blocks: int = 1, + local_blocks: int = 2, +) -> torch.Tensor: + num_k_heads, total_query_len, max_key_len = score.shape + batch_size = cu_seqlens_q.shape[0] - 1 + pad_len = kernel_size // kernel_stride - 1 + max_blocks = math.ceil(max_seqlen_q / block_size) + block_score = torch.zeros( + num_k_heads, + total_query_len, + max_blocks, + dtype=torch.float32, + device=score.device, + ) + offs = ( + torch.arange(kernel_size // kernel_stride, device=score.device)[:, None] + + torch.arange(block_size // kernel_stride, device=score.device)[None, :] + ).view(-1) + offs = torch.histc(offs, bins=offs.max() + 1, min=0, max=offs.max()) + num_offs = int(offs.shape[0]) + BLOCK_SIZE_K = min(128, triton.next_power_of_2(max_blocks)) + BLOCK_SIZE_O = triton.next_power_of_2(num_offs) + BLOCK_SIZE_Q = 8 + grid = ( + num_k_heads * batch_size, + triton.cdiv(total_query_len, BLOCK_SIZE_Q), + triton.cdiv(max_blocks, BLOCK_SIZE_K), + ) + _transform_score_kernel[grid]( + score, + block_score, + torch.ones_like(offs, dtype=offs.dtype,device=offs.device), #! 为了max 就不用wieght了 + cu_seqlens_q, + num_k_heads, + offs.shape[0], + max_key_len, + max_blocks, + pad_len, + block_size, + block_size // kernel_stride, + init_blocks, + local_blocks, + score.stride(0), + score.stride(1), + score.stride(2), + block_score.stride(0), + block_score.stride(1), + block_score.stride(2), + BLOCK_SIZE_Q=BLOCK_SIZE_Q, + BLOCK_SIZE_K=BLOCK_SIZE_K, + BLOCK_SIZE_O=BLOCK_SIZE_O, + num_warps=8, + num_stages=3, + ) + return block_score + + +def compressed_attention( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + kernel_size: int, + kernel_stride: int, + block_size: int, + topk: int, + cu_seqlens_q: torch.Tensor, + cu_seqlens_k: torch.Tensor, + max_seqlen_q: int, + max_seqlen_k: int, + sm_scale: float = None, + init_blocks: int = 1, + local_blocks: int = 2, + parallel_topk_compute: Union[str, bool] = "auto", +) -> Tuple[torch.Tensor, torch.Tensor]: + """Attention between query and compressed key and value. Compute attention output and topk block idx used in topk_sparse_attention. + + Args: + q (torch.Tensor): shape [total_q_len, num_q_heads, head_dim] + k (torch.Tensor): shape [total_kv_len, num_kv_heads, head_dim] + v (torch.Tensor): shape [total_kv_len, num_kv_heads, head_dim] + kernel_size (int): kernel size in compress_key_value + kernel_stride (int): stride of compress_key_value + block_size (int): key value block size for topk sparse attention. + topk (int): number of blocks for each query. + cu_seqlens_q (torch.Tensor): shape [batch_size + 1], similar to cu_seqlens_q in flash_attn_func_varlen. + cu_seqlens_k (torch.Tensor): shape [batch_size + 1], similar to cu_seqlens_k in flash_attn_func_varlen. + max_seqlen_q (int): max q len of the batch. + max_seqlen_k (int): max k len of the batch. + sm_scale (float, optional): softmax scale. Defaults to None, means 1/sqrt(head_dim). + init_blocks (int, optional): Number of init blocks for each query. Defaults to 1. + local_blocks (int, optional): Number of local blocks for each query. Defaults to 2. + parallel_topk_compute (str, optional): Only set it to False when the sequence length is too long. This can avoid a current bug. + We'll fix this issue later. Defaults to auto, it will be set to False when the sequence length is greater than 32k and True otherwise. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: attention output and topk_idx used in topk_sparse_attention + """ + if max_seqlen_q is None: + max_seqlen_q = (cu_seqlens_q[1:] - cu_seqlens_q[:-1]).max().item() + if max_seqlen_k is None: + max_seqlen_k = (cu_seqlens_k[1:] - cu_seqlens_k[:-1]).max().item() + attn_output, lse = CompressedAttention.apply( + q, + k, + v, + kernel_size, + kernel_stride, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_q, + max_seqlen_k, + sm_scale, + ) + + # do not select topk index + if topk <= 0: + warnings.warn("topk <= 0, returned topk_idx will be None") + return attn_output, None + + assert topk >= init_blocks #+ local_blocks + with torch.no_grad(): + num_k_heads, num_q_heads = k.shape[1], q.shape[1] + num_shared_q_heads = num_q_heads // num_k_heads + batch_size = cu_seqlens_q.shape[0] - 1 + q_idx = torch.cat( + [ + torch.arange(cu_seqlens_q[i + 1] - cu_seqlens_q[i], device=q.device) + for i in range(batch_size) + ], + dim=0, + ) + q_idx = q_idx // block_size + # whether to use parallel version + if parallel_topk_compute == "auto": + parallel_topk_compute = cu_seqlens_q[-1] <= 32768 + # parallel version + if parallel_topk_compute: + # recompute score + score = _get_attention_score( + q, + k, + lse, + kernel_size, + kernel_stride, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_q, + max_seqlen_k, + sm_scale, + ) + # transform score to block-wise score + score = transform_score( + score, + kernel_size, + kernel_stride, + block_size, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_q, + max_seqlen_k, + init_blocks, + local_blocks, + ) + # get topk + topk = min(topk, score.shape[-1]) + topk_idx = score.topk(topk, dim=-1).indices.sort(-1).values + # print(cu_seqlens_q) + # breakpoint() + topk_idx[topk_idx >= q_idx[None, :, None]] = -1 + topk_idx = topk_idx.to(torch.int32) + # non parallel version, avoid some current bugs when sequence length is too long + # FIXME: need to fix later + else: + topk_idx_list = [] + for h in range(num_k_heads): + # recompute score + score = _get_attention_score( + q[:, h * num_shared_q_heads : (h + 1) * num_shared_q_heads], + k[:, h : h + 1], + lse[h * num_shared_q_heads : (h + 1) * num_shared_q_heads], + kernel_size, + kernel_stride, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_q, + max_seqlen_k, + sm_scale, + ) + # transform score to block-wise score + score = transform_score( + score, + kernel_size, + kernel_stride, + block_size, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_q, + max_seqlen_k, + init_blocks, + local_blocks, + ) + # get topk + topk = min(topk, score.shape[-1]) + topk_idx = score.topk(topk, dim=-1).indices.sort(-1).values + topk_idx[topk_idx >= q_idx[None, :, None]] = -1 + topk_idx = topk_idx.to(torch.int32) + topk_idx_list.append(topk_idx) + topk_idx = torch.cat(topk_idx_list, dim=0) + return attn_output, topk_idx diff --git a/config.json b/config.json new file mode 100644 index 0000000..84bca73 --- /dev/null +++ b/config.json @@ -0,0 +1,177 @@ +{ + "_name_or_path": "/share_data/data7/fanshengda/mcp-agent/minicpm4_sft/mcp_summary/checkpoint-25000", + "architectures": [ + "MiniCPMForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "auto_map": { + "AutoConfig": "configuration_minicpm.MiniCPMConfig", + "AutoModel": "modeling_minicpm.MiniCPMForCausalLM", + "AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM", + "AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM", + "AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification" + }, + "bos_token_id": 1, + "dim_model_base": 256, + "eos_token_id": [ + 2, + 73440 + ], + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.1, + "intermediate_size": 16384, + "max_position_embeddings": 32768, + "model_type": "minicpm", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 2, + "pad_token_id": 2, + 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+ "scale_depth": 1.4, + "scale_emb": 12, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.49.0", + "use_cache": true, + "vocab_size": 73448 +} diff --git a/configuration.json b/configuration.json new file mode 100644 index 0000000..f9291c3 --- /dev/null +++ b/configuration.json @@ -0,0 +1 @@ +{"framework":"Pytorch","task":"text-generation"} \ No newline at end of file diff --git a/configuration_minicpm.py b/configuration_minicpm.py new file mode 100644 index 0000000..f45b355 --- /dev/null +++ b/configuration_minicpm.py @@ -0,0 +1,197 @@ +# coding=utf-8 +# Copyright 2025 The OpenBMB Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" MiniCPM model configuration""" + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +MINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {} + + +class MiniCPMConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the MiniCPM-7B. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 32000): + Vocabulary size of the MiniCPM model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`MiniCPMModel`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 11008): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer decoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer decoder. + num_key_value_heads (`int`, *optional*): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to + `num_attention_heads`. + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 2048): + The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens, + MiniCPM 2 up to 4096, CodeMiniCPM up to 16384. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + rms_norm_eps (`float`, *optional*, defaults to 1e-06): + The epsilon used by the rms normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. + pad_token_id (`int`, *optional*): + Padding token id. + bos_token_id (`int`, *optional*, defaults to 1): + Beginning of stream token id. + eos_token_id (`int`, *optional*, defaults to 2): + End of stream token id. + pretraining_tp (`int`, *optional*, defaults to 1): + Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this + document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is + necessary to ensure exact reproducibility of the pretraining results. Please refer to [this + issue](https://github.com/pytorch/pytorch/issues/76232). + tie_word_embeddings (`bool`, *optional*, defaults to `False`): + Whether to tie weight embeddings + rope_theta (`float`, *optional*, defaults to 10000.0): + The base period of the RoPE embeddings. + rope_scaling (`Dict`, *optional*): + Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling + strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is + `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update + `max_position_embeddings` to the expected new maximum. See the following thread for more information on how + these scaling strategies behave: + https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an + experimental feature, subject to breaking API changes in future versions. + attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): + Whether to use a bias in the query, key, value and output projection layers during self-attention. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + + ```python + >>> from transformers import MiniCPMModel, MiniCPMConfig + + >>> # Initializing a MiniCPM minicpm-7b style configuration + >>> configuration = MiniCPMConfig() + + >>> # Initializing a model from the minicpm-7b style configuration + >>> model = MiniCPMModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "minicpm" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=32000, + hidden_size=4096, + intermediate_size=11008, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=None, + hidden_act="silu", + max_position_embeddings=2048, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + pad_token_id=None, + bos_token_id=1, + eos_token_id=2, + pretraining_tp=1, + tie_word_embeddings=True, + rope_theta=10000.0, + rope_scaling=None, + attention_bias=False, + attention_dropout=0.0, + scale_emb=1, + dim_model_base=1, + scale_depth=1, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.pretraining_tp = pretraining_tp + self.use_cache = use_cache + self.rope_theta = rope_theta + self.rope_scaling = rope_scaling + # self._rope_scaling_validation() + self.attention_bias = attention_bias + self.attention_dropout = attention_dropout + self.scale_emb = scale_emb + self.dim_model_base = dim_model_base + self.scale_depth = scale_depth + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + try: + import flash_attn + self._attn_implementation = "flash_attention_2" + except: + pass + + def _rope_scaling_validation(self): + """ + Validate the `rope_scaling` configuration. + """ + if self.rope_scaling is None: + return + + if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: + raise ValueError( + "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, " + f"got {self.rope_scaling}" + ) + rope_scaling_type = self.rope_scaling.get("type", None) + rope_scaling_factor = self.rope_scaling.get("factor", None) + if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: + raise ValueError( + f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" + ) + if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: + raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}") diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..266fa26 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,12 @@ +{ + "bos_token_id": 1, + "do_sample": true, + "eos_token_id": [ + 2, + 73440 + ], + "pad_token_id": 2, + "temperature": 0.8, + "top_p": 0.8, + "transformers_version": "4.49.0" +} diff --git a/model-00001-of-00005.safetensors b/model-00001-of-00005.safetensors new file mode 100644 index 0000000..684cd8d --- /dev/null +++ b/model-00001-of-00005.safetensors @@ -0,0 +1,3 @@ +version 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All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch MiniCPM model.""" +import math +import warnings +from typing import Any, List, Optional, Tuple, Union, Dict +from einops import rearrange, einsum +import numpy as np +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn, tensor +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache,DynamicCache +from transformers.modeling_attn_mask_utils import ( + AttentionMaskConverter, + _prepare_4d_attention_mask, + _prepare_4d_causal_attention_mask, + _prepare_4d_causal_attention_mask_for_sdpa, +) +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13 +from transformers.utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_2_available, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) +from transformers.utils.import_utils import is_torch_fx_available + +from .configuration_minicpm import MiniCPMConfig +import re + +try: + from flash_attn import flash_attn_func, flash_attn_varlen_func + from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa +except: + pass + +#! nsa +#! debug token +debug_token=3 +token_now =0 +save_no_cache =False +save_cache=False +from functools import lru_cache +from .compressed_attention import compressed_attention +from block_sparse_attn import ( + block_sparse_attn_func,block_sparse_attn_kvcache_func +) + +def prepare_fa2_from_position_ids(query, key, value, position_ids): + """ + This function returns necessary arguments to call `flash_attn_varlen_func`. + All three query, key, value states will be flattened. + Cumulative lengths of each examples in the batch will be extracted from position_ids. + + NOTE: ideally cumulative lengths should be prepared at the data collator stage + + Arguments: + query (`torch.Tensor`): + Query state with padding. Shape: (batch_size, query_length, num_heads, head_dim). + key (`torch.Tensor`): + Key state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim). + value (`torch.Tensor`): + Value state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim). + position_ids (`torch.Tensor`): + Boolean or int tensor of shape (batch_size, sequence_length), 1 means valid and 0 means not valid. + + Return: + query (`torch.Tensor`): + Query state without padding. Shape: (total_target_length, num_heads, head_dim). + key (`torch.Tensor`): + Key state with padding. Shape: (total_source_length, num_key_value_heads, head_dim). + value (`torch.Tensor`): + Value state with padding. Shape: (total_source_length, num_key_value_heads, head_dim). + indices_q (`torch.Tensor`): + The indices of non-masked tokens from the flattened input target sequence. + (cu_seqlens_q, cu_seqlens_k) (`Tuple[int]`): + The cumulative sequence lengths for the target (query) and source (key, value), used to index into ragged (unpadded) tensors. `cu_seqlens` shape is (batch_size + 1,). + (max_seqlen_in_batch_q, max_seqlen_in_batch_k) (`Tuple[int]`): + Maximum sequence length in batch (`max_seqlen_in_batch_q` for the target sequence i.e. query, `max_seqlen_in_batch_k` for the source sequence i.e. key/value). + """ + query = query.view(-1, query.size(-2), query.size(-1)) + key = key.contiguous().view(-1, key.size(-2), key.size(-1)) + value = value.contiguous().view(-1, value.size(-2), value.size(-1)) + position_ids = position_ids.flatten() + indices_q = torch.arange(position_ids.size(0), device=position_ids.device, dtype=torch.int32) + + cu_seq_lens = torch.cat( + ( + indices_q[position_ids == 0], + torch.tensor(position_ids.size(), device=position_ids.device, dtype=torch.int32), + ) + ) + + max_length = position_ids.max() + 1 + + return (query, key, value, indices_q, (cu_seq_lens, cu_seq_lens), (max_length, max_length)) + + +def convert_topk_to_base_blockmask( + topk_idx: torch.Tensor, + max_seqlen_k: int, + block_size: int, + device: str = "cuda" +) -> torch.Tensor: + """ + 将topk索引转换为块稀疏注意力掩码，仅处理-1的情况 + + Args: + topk_idx: 形状 [num_heads, total_seqlen, k] 的块索引张量 + cu_seqlens_q: 累积序列长度（用于计算总长度） + max_seqlen_k: 最大键序列长度（用于计算键块数量） + block_size: block_size + device: 输出设备 + + Returns: + mask: 布尔掩码，形状 [num_heads, total_seqlen, k_blocks] + """ + # 计算键块数量 + k_blocks = (max_seqlen_k + block_size - 1) // block_size # 向上取整 + num_heads, total_seqlen, k = topk_idx.shape + + # 初始化全False掩码 + mask = torch.zeros(num_heads, total_seqlen, k_blocks, + dtype=torch.bool, device=device) + + # 过滤掉 -1，确保索引合法 + valid_idx = topk_idx[topk_idx != -1] + + # 生成索引掩码 + batch_idx, seq_idx, _ = torch.where(topk_idx != -1) # 找到非-1索引的 (head, seq) 位置 + mask[batch_idx, seq_idx, valid_idx] = True # 设置对应位置为 True + + return mask + + +@lru_cache(maxsize=16) +def calc_chunks_with_stride(cu_seqlen, moba_chunk_size, kernel_stride): + """ + 计算需要 MOBA 注意力的 chunks，支持 stride。 + 返回: + - filtered_indices: 用于直接索引 kv 的索引。 + - cu_seqlens_compressed: 压缩后的累积序列长度。 + """ + # 1. 计算每个序列的长度 + batch_sizes = cu_seqlen[1:] - cu_seqlen[:-1] + + # 2. 计算每个序列的 chunk 起始位置 (考虑 stride) + max_seq_len = torch.max(batch_sizes) + max_num_chunks_per_seq = (max_seq_len - moba_chunk_size) // kernel_stride + 1 # 修正公式 + chunk_start_offsets = torch.arange(0, max_num_chunks_per_seq * kernel_stride, kernel_stride, device=cu_seqlen.device) + seq_starts = cu_seqlen[:-1] + chunk_start_in_seq = seq_starts[:, None] + chunk_start_offsets[None, :] # [batch_size, max_num_chunks_per_seq] + + # 3. 过滤掉超出序列长度的 chunk 和非完整大小的 chunk + chunk_end_in_seq = chunk_start_in_seq + moba_chunk_size + valid_chunk_mask = (chunk_end_in_seq <= (seq_starts[:, None] + batch_sizes[:, None])) # 完整 chunk + + # 4. 根据 valid_chunk_mask 过滤有效的 chunk 起始位置 + valid_chunk_starts = chunk_start_in_seq[valid_chunk_mask] # [num_valid_chunks] + del chunk_start_in_seq + # 5. 生成 filtered_indices + chunk_indices = torch.arange( + 0, moba_chunk_size, device=cu_seqlen.device + )[None, :] # [1, moba_chunk_size] + filtered_indices = valid_chunk_starts[:, None] + chunk_indices # [num_valid_chunks, moba_chunk_size] + filtered_indices = filtered_indices.view(-1) # 展平为一维索引 + + # 6. 计算压缩后的累积序列长度 + num_filtered_chunks_per_batch = valid_chunk_mask.sum(dim=1) # 每个 batch 的有效 chunk 数量 + cu_seqlens_compressed = torch.zeros( + len(cu_seqlen), dtype=torch.int32, device=cu_seqlen.device + ) + cu_seqlens_compressed[1:] = num_filtered_chunks_per_batch.cumsum(dim=0) + del num_filtered_chunks_per_batch, chunk_start_offsets, seq_starts, chunk_end_in_seq, valid_chunk_mask, chunk_indices + return filtered_indices, cu_seqlens_compressed + + +class CompressKV(torch.nn.Module): + def __init__(self, head_num_k, head_dim, kernel_size, compress_func, add_pos_embed=False, kernel_stride=16): + """ + 压缩KV模块，支持多种压缩方式 + Args: + head_num_k: KV头的数量 + head_dim: 每个头的维度 + kernel_size: 每个chunk的大小 + compress_func: 压缩方式（如meanpool, mlp, conv1d等） + add_pos_embed: 是否添加位置编码 + stride: 分块时的步长 + """ + super().__init__() + self.kernel_size = kernel_size + self.compress_func = compress_func + self.head_num_k = head_num_k + self.head_dim = head_dim + self.kernel_stride = kernel_stride # 新增stride参数 + + # 定义不同的压缩方式 + if compress_func == 'mlp' or compress_func == 'mlp+residual': + self.kv_compress = nn.Sequential( + nn.Linear(kernel_size * 2, kernel_size * 4), + nn.ReLU(), + nn.Linear(kernel_size * 4, 2) + ) + elif compress_func == 'conv1d': + self.k_conv = nn.Conv1d(in_channels=self.head_dim, out_channels=self.head_dim, kernel_size=kernel_size) + self.v_conv = nn.Conv1d(in_channels=self.head_dim, out_channels=self.head_dim, kernel_size=kernel_size) + elif compress_func == 'weighted_sum': + self.weight_net_v = nn.Linear(self.head_dim, 1) + self.weight_net_k = nn.Linear(self.head_dim, 1) + elif compress_func == 'weighted_sum+proj': + self.weight_net_v = nn.Linear(self.head_dim, 1) + self.weight_net_k = nn.Linear(self.head_dim, 1) + self.k_proj = nn.Linear(self.head_dim, self.head_dim) + self.v_proj = nn.Linear(self.head_dim, self.head_dim) + + if add_pos_embed: + # 修改位置编码层：为每个头创建独立的位置编码 + self.pos_embed = nn.Embedding( + kernel_size, + head_num_k * head_dim # 维度扩展为 [kernel_size, num_heads * head_dim] + ) + else: + self.pos_embed = None + + def forward(self, kv: torch.Tensor, cu_seqlens): + """ + 前向传播，压缩KV + Args: + kv: 输入的KV张量 + cu_seqlens: 累积序列长度 + Returns: + compress_k: 压缩后的K + compress_v: 压缩后的V + cu_seqlens_compressed: 压缩后的累积序列长度 + """ + + # 计算chunk相关信息，支持stride + filtered_kv_indices, cu_seqlens_compressed = calc_chunks_with_stride( + cu_seqlens, self.kernel_size, self.kernel_stride + ) + + # 提取过滤后的kv + filtered_kv = kv.index_select(0, filtered_kv_indices.view(-1)) + + # 分块 + filtered_kv = filtered_kv.view( filtered_kv.shape[0]// self.kernel_size, self.kernel_size, 2, self.head_num_k, self.head_dim) #[l, block_size,2,h,d] + if self.pos_embed is not None: + positions = torch.arange(self.kernel_size, device=kv.device) + pos_emb = self.pos_embed(positions) # [kernel_size, num_heads * head_dim] + + # 重塑形状以匹配多头结构 + pos_emb = pos_emb.view( + self.kernel_size, + self.head_num_k, # 使用实际头数参数（需在__init__中保存） + self.head_dim + ) # [kernel_size, num_heads, head_dim] + + # 添加维度用于广播 + pos_emb = pos_emb.reshape(1,self.kernel_size,1, self.head_num_k, self.head_dim) # [1, block_size, 1, num_heads, head_dim] + filtered_kv = filtered_kv + pos_emb + + if self.compress_func == "meanpool": + compressed_kv = filtered_kv.mean(dim=1) + compress_k = compressed_kv[:, 0, :, :]#.reshape(-1, self.head_num_k, self.head_dim) + compress_v = compressed_kv[:, 1, :, :]#.reshape(-1, self.head_num_k, self.head_dim) + elif self.compress_func == "mlp": + + filtered_kv = filtered_kv.permute(0, 3,4,2, 1).reshape(filtered_kv.shape[0], self.head_num_k, self.head_dim,-1) + compressed_kv = self.kv_compress(filtered_kv) + compress_k = compressed_kv[:, :, :, 0]#.reshape(-1, self.head_num_k, self.head_dim) + compress_v = compressed_kv[:, :, :, 1]#.reshape(-1, self.head_num_k, self.head_dim) + elif self.compress_func == "mlp+residual": + mean_kv = filtered_kv.mean(dim=1) + mlp_kv = self.kv_compress(filtered_kv.permute(0, 3,4,2, 1).reshape(filtered_kv.shape[0], self.head_num_k, self.head_dim,-1)).permute(0, 3,1,2) #[l, h,d,2]->[l,2,h,d] + compressed_kv = mean_kv + mlp_kv + compress_k = compressed_kv[:, 0, :, :] + compress_v = compressed_kv[:, 1, :, :] + elif self.compress_func == 'conv1d': + k = filtered_kv[:,: ,0,:, :] + k = rearrange(k, 'l block_size h d -> (l h) d block_size') #只能3维 + v = filtered_kv[:,: ,1,:, :] + v = rearrange(v, 'l block_size h d -> (l h) d block_size') + compress_k = self.k_conv(k).squeeze(-1) # [(l h), d] + compress_v = self.v_conv(v).squeeze(-1) # [(l h), d] + compress_k = rearrange(compress_k, '(l h) d -> l h d', h=self.head_num_k) + compress_v = rearrange(compress_v, '(l h) d -> l h d', h=self.head_num_k) + + elif self.compress_func == 'weighted_sum': + k = filtered_kv[:,: ,0,:, :] + k = rearrange(k, 'l block_size h d -> l h block_size d') + v = filtered_kv[:,: ,1,:, :] + v = rearrange(v, 'l block_size h d -> l h block_size d') + weight_k = torch.softmax(self.weight_net_k(k), dim=2) # [l, h, block_size, 1] + weight_v = torch.softmax(self.weight_net_v(v), dim=2) # [l, h, block_size, 1] + + compress_k = (weight_k * k).sum(dim=2) # [l, h, d] + compress_v = (weight_v * v).sum(dim=2) # [l, h, d] + elif self.compress_func == 'weighted_sum+proj': + k = filtered_kv[:,: ,0,:, :] + k = rearrange(k, 'l block_size h d -> l h block_size d') + v = filtered_kv[:,: ,1,:, :] + v = rearrange(v, 'l block_size h d -> l h block_size d') + weight_k = torch.softmax(self.weight_net_k(k), dim=2) # [l, h, block_size, 1] + weight_v = torch.softmax(self.weight_net_v(v), dim=2) # [l, h, block_size, 1] + + compress_k = (weight_k * self.k_proj(k)).sum(dim=2) # [l, h, d] + compress_v = (weight_v * self.v_proj(v)).sum(dim=2) # [l, h, d] + + + else: + raise ValueError(f"Unsupported compress type: {self.compress_func}") + + del filtered_kv + if 'compressed_kv' in locals(): del compressed_kv + + return compress_k, compress_v, cu_seqlens_compressed +class DynamicCacheQKV(DynamicCache): + """ + A cache that grows dynamically as more tokens are generated. This is the default for generative models. + + It stores the Key and Value states as a list of tensors, one for each layer. The expected shape for each tensor is + `[batch_size, num_heads, seq_len, head_dim]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache + + >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct") + >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct") + + >>> inputs = tokenizer(text="My name is Qwen2", return_tensors="pt") + + >>> # Prepare a cache class and pass it to model's forward + >>> past_key_values = DynamicCache() + >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True) + >>> outputs.past_key_values # access cache filled with key/values from generation + DynamicCache() + ``` + """ + + def __init__(self, num_hidden_layers: Optional[int] = None) -> None: + super().__init__() + if num_hidden_layers is None: + self.key_cache: List[torch.Tensor] = [] + self.value_cache: List[torch.Tensor] = [] + self.query_cache: List[torch.Tensor] = [] + else: + self.key_cache: List[torch.Tensor] = [[] for _ in range(num_hidden_layers)] + self.value_cache: List[torch.Tensor] = [[] for _ in range(num_hidden_layers)] + self.query_cache: List[torch.Tensor] = [[] for _ in range(num_hidden_layers)] + self._seen_tokens = 0 # Used in `generate` to keep tally of how many tokens the cache has seen + + def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]: + """ + Support for backwards-compatible `past_key_value` indexing, e.g. `past_key_value[0][0].shape[2]` to get the + sequence length. + """ + if layer_idx < len(self): + return (self.key_cache[layer_idx], self.value_cache[layer_idx],self.query_cache[layer_idx]) + else: + raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}") + + def __iter__(self): + """ + Support for backwards-compatible `past_key_value` iteration, e.g. `for x in past_key_value:` to iterate over + keys and values + """ + for layer_idx in range(len(self)): + yield (self.key_cache[layer_idx], self.value_cache[layer_idx],self.query_cache[layer_idx]) + + def __len__(self): + """ + Support for backwards-compatible `past_key_value` length, e.g. `len(past_key_value)`. This value corresponds + to the number of layers in the model. + """ + return len(self.key_cache) + + def update( + self, + key_states: torch.Tensor, + value_states: torch.Tensor, + layer_idx: int, + cache_kwargs: Optional[Dict[str, Any]] = None,query_states: torch.Tensor=None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`. + + Parameters: + key_states (`torch.Tensor`): + The new key states to cache. + value_states (`torch.Tensor`): + The new value states to cache. + layer_idx (`int`): + The index of the layer to cache the states for. + cache_kwargs (`Dict[str, Any]`, `optional`): + Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`. + + Return: + A tuple containing the updated key and value states. + """ + # Update the number of seen tokens + if layer_idx == 0: + self._seen_tokens += key_states.shape[-2] + if query_states is None: + raise ValueError("query_states must be provided for DynamicCacheQKV") + + # Update the cache + if len(self.key_cache) <= layer_idx: + self.key_cache.append(key_states) + self.value_cache.append(value_states) + self.query_cache.append(query_states) + # content on layer cache can be a tensor and checking not tensor causes errors + # so we explicitly check for the empty list + elif self.key_cache[layer_idx] == []: + self.key_cache[layer_idx] = key_states + self.value_cache[layer_idx] = value_states + self.query_cache[layer_idx] = query_states + else: + self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2) + self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2) + self.query_cache[layer_idx] = torch.cat([self.query_cache[layer_idx], query_states], dim=-2) + + return self.key_cache[layer_idx], self.value_cache[layer_idx], self.query_cache[layer_idx] + + def get_seq_length(self, layer_idx: Optional[int] = 0) -> int: + """Returns the sequence length of the cached states. A layer index can be optionally passed.""" + # TODO: deprecate this function in favor of `cache_position` + if len(self.key_cache) <= layer_idx or (len(self.key_cache) > layer_idx and self.key_cache[layer_idx] == []): + return 0 + return self.key_cache[layer_idx].shape[-2] + + def get_max_length(self) -> Optional[int]: + """Returns the maximum sequence length of the cached states. DynamicCache does not have a maximum length.""" + return None + + def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]: + """Converts the `DynamicCache` instance into the its equivalent in the legacy cache format. Used for + backward compatibility.""" + legacy_cache = () + for layer_idx in range(len(self)): + legacy_cache += ((self.key_cache[layer_idx], self.value_cache[layer_idx]),) + return legacy_cache + + # @classmethod + # def from_legacy_cache( + # cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, num_hidden_layers: int = None + # ) -> "DynamicCacheQKV": + # """Converts a cache in the legacy cache format into an equivalent `DynamicCache`. Used for + # backward compatibility.""" + # cache = cls(num_hidden_layers) + # if past_key_values is not None: + # for layer_idx in range(len(past_key_values)): + # key_states, value_states, query_status = past_key_values[layer_idx] + # cache.update(key_states, value_states, query_status,layer_idx) + # return cache + + def crop(self, max_length: int): + """Crop the past key values up to a new `max_length` in terms of tokens. `max_length` can also be + negative to remove `max_length` tokens. This is used in assisted decoding and contrastive search.""" + # In case it is negative + if max_length < 0: + max_length = self.get_seq_length() - abs(max_length) + + if self.get_seq_length() <= max_length: + return + + self._seen_tokens = max_length + for idx in range(len(self.key_cache)): + if self.key_cache[idx] != []: + self.key_cache[idx] = self.key_cache[idx][..., :max_length, :] + self.value_cache[idx] = self.value_cache[idx][..., :max_length, :] + self.query_cache[idx] = self.query_cache[idx][..., :max_length, :] + + def batch_split(self, full_batch_size: int, split_size: int, num_hidden_layers: int) -> List["DynamicCacheQKV"]: + """Split the current instance into a list of `DynamicCache` by the batch size. This will be used by + `_split_model_inputs()` in `generation.utils`""" + out = [] + for i in range(0, full_batch_size, split_size): + current_split = DynamicCacheQKV(num_hidden_layers) + current_split._seen_tokens = self._seen_tokens + current_split.key_cache = [tensor[i : i + split_size] for tensor in self.key_cache] + current_split.value_cache = [tensor[i : i + split_size] for tensor in self.value_cache] + current_split.query_cache = [tensor[i : i + split_size] for tensor in self.query_cache] + out.append(current_split) + return out + + @classmethod + def from_batch_splits(cls, splits: List["DynamicCacheQKV"], num_hidden_layers: int) -> "DynamicCacheQKV": + """This is the opposite of the above `batch_split()` method. This will be used by `stack_model_outputs` in + `generation.utils`""" + cache = cls(num_hidden_layers) + for idx in range(len(splits[0])): + key_cache = [current.key_cache[idx] for current in splits if current.key_cache[idx] != []] + value_cache = [current.key_cache[idx] for current in splits if current.key_cache[idx] != []] + query_cache = [current.key_cache[idx] for current in splits if current.key_cache[idx] != []] + if key_cache != []: + layer_keys = torch.cat(key_cache, dim=0) + layer_values = torch.cat(value_cache, dim=0) + layer_query = torch.cat(query_cache, dim=0) + cache.update(layer_keys, layer_values, idx,query_states=layer_query) + return cache + + def batch_repeat_interleave(self, repeats: int): + """Repeat the cache `repeats` times in the batch dimension. Used in contrastive search.""" + for layer_idx in range(len(self)): + self.key_cache[layer_idx] = self.key_cache[layer_idx].repeat_interleave(repeats, dim=0) + self.value_cache[layer_idx] = self.value_cache[layer_idx].repeat_interleave(repeats, dim=0) + self.query_cache[layer_idx] = self.query_cache[layer_idx].repeat_interleave(repeats, dim=0) + + def batch_select_indices(self, indices: torch.Tensor): + """Only keep the `indices` in the batch dimension of the cache. Used in contrastive search.""" + for layer_idx in range(len(self)): + self.key_cache[layer_idx] = self.key_cache[layer_idx][indices, ...] + self.value_cache[layer_idx] = self.value_cache[layer_idx][indices, ...] + self.query_cache[layer_idx] = self.query_cache[layer_idx][indices, ...] + +#! nsa +# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph. +# It means that the function will not be traced through and simply appear as a node in the graph. +if is_torch_fx_available(): + if not is_torch_greater_or_equal_than_1_13: + import torch.fx + + _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask) + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "MiniCPMConfig" + + +def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + +def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): + warnings.warn( + "Calling `transformers.models.minicpm.modeling_minicpm._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask" + ) + return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len) + + +def _make_causal_mask( + input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 +): + warnings.warn( + "Calling `transformers.models.minicpm.modeling_minicpm._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.minicpm.modeling_minicpm.AttentionMaskConverter._make_causal_mask" + ) + return AttentionMaskConverter._make_causal_mask( + input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length + ) + +# @torch.jit.script # type: ignore +def rms_layernorm(hidden: torch.Tensor, weight: torch.Tensor, eps: float): + old_dtype = hidden.dtype + variance = hidden.to(torch.float32).pow(2).mean(dim=-1, keepdim=True) + hidden = (hidden * torch.rsqrt(variance + eps)).to(old_dtype) + return hidden * weight + + +class MiniCPMRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + MiniCPMRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + return rms_layernorm(hidden_states, self.weight, self.variance_epsilon) + + +ALL_LAYERNORM_LAYERS.append(MiniCPMRMSNorm) + + +class MiniCPMRotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + # seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32 + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:seq_len].to(dtype=x.dtype), + self.sin_cached[:seq_len].to(dtype=x.dtype), + ) + + +class MiniCPMLongRoPE(MiniCPMRotaryEmbedding): + """MiniCPMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, short_factor=None, long_factor=None, original_max_position_embeddings=None): + self.short_factor = short_factor + self.long_factor = long_factor + self.original_max_position_embeddings = original_max_position_embeddings + scale = (max_position_embeddings / + self.original_max_position_embeddings) + self.scaling_factor = math.sqrt( + 1 + math.log(scale) / + math.log(self.original_max_position_embeddings)) + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + if seq_len > self.original_max_position_embeddings: + ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=device) + else: + ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=device) + + freqs = torch.mul( + torch.outer(t, 1.0 / ext_factors).to(device=device), + self.inv_freq.to(device=device).to(dtype) + ) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype) * self.scaling_factor, persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype) * self.scaling_factor, persistent=False) + + +class MiniCPMLinearScalingRotaryEmbedding(MiniCPMRotaryEmbedding): + """MiniCPMRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + t = t / self.scaling_factor + + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + +class MiniCPMDynamicNTKScalingRotaryEmbedding(MiniCPMRotaryEmbedding): + """MiniCPMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + + if seq_len > self.max_position_embeddings: + base = self.base * ( + (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) + ) ** (self.dim / (self.dim - 2)) + inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`): + The position indices of the tokens corresponding to the query and key tensors. For example, this can be + used to pass offsetted position ids when working with a KV-cache. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + # cos = cos[position_ids].unsqueeze(unsqueeze_dim) + # sin = sin[position_ids].unsqueeze(unsqueeze_dim) + # q_embed = (q * cos) + (rotate_half(q) * sin) + # k_embed = (k * cos) + (rotate_half(k) * sin) + orig_dtype = k.dtype + cos = cos[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim] + sin = sin[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim] + q_fp32 = q.to(dtype=torch.float32, device=q.device) + k_fp32 = k.to(dtype=torch.float32, device=k.device) + q_embed = (q_fp32 * cos) + (rotate_half(q_fp32) * sin) + k_embed = (k_fp32 * cos) + (rotate_half(k_fp32) * sin) + return q_embed.to(dtype=orig_dtype), k_embed.to(dtype=orig_dtype) + +class MiniCPMMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + if self.config.pretraining_tp > 1: + slice = self.intermediate_size // self.config.pretraining_tp + gate_proj_slices = self.gate_proj.weight.split(slice, dim=0) + up_proj_slices = self.up_proj.weight.split(slice, dim=0) + down_proj_slices = self.down_proj.weight.split(slice, dim=1) + + gate_proj = torch.cat( + [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1 + ) + up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1) + + intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2) + down_proj = [ + F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp) + ] + down_proj = sum(down_proj) + else: + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + return down_proj + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + + +class MiniCPMAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: MiniCPMConfig, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " + "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.attention_dropout = config.attention_dropout + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + self.is_causal = True + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias) + self._init_rope() + + def _init_rope(self): + if self.config.rope_scaling is None: + self.rotary_emb = MiniCPMRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + base=self.rope_theta, + ) + else: + scaling_type = self.config.rope_scaling["rope_type"] + scaling_factor = self.config.rope_scaling.get("factor", None) + if scaling_type == "linear": + self.rotary_emb = MiniCPMLinearScalingRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + scaling_factor=scaling_factor, + base=self.rope_theta, + ) + elif scaling_type == "dynamic": + self.rotary_emb = MiniCPMDynamicNTKScalingRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + scaling_factor=scaling_factor, + base=self.rope_theta, + ) + elif scaling_type == "longrope": + self.rotary_emb = MiniCPMLongRoPE( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + short_factor = self.config.rope_scaling["short_factor"], + long_factor = self.config.rope_scaling["long_factor"], + base=self.rope_theta, + original_max_position_embeddings=self.config.rope_scaling["original_max_position_embeddings"] + ) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + + bsz, q_len, _ = hidden_states.size() + + if self.config.pretraining_tp > 1: + key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp + query_slices = self.q_proj.weight.split( + (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0 + ) + key_slices = self.k_proj.weight.split(key_value_slicing, dim=0) + value_slices = self.v_proj.weight.split(key_value_slicing, dim=0) + + query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)] + query_states = torch.cat(query_states, dim=-1) + + key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)] + key_states = torch.cat(key_states, dim=-1) + + value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)] + value_states = torch.cat(value_states, dim=-1) + + else: + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + if self.config.pretraining_tp > 1: + attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2) + o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1) + attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)]) + else: + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class MiniCPMFlashAttention2(MiniCPMAttention): + """ + MiniCPM flash attention module. This module inherits from `MiniCPMAttention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + #! -------nsa------- + self.kernel_size = 32 + self.kernel_stride = 16 + compress_type = 'meanpool' + self.init_blocks=1 + + self.block_size=64 + + self.window_size=2048 + + self.local_blocks = self.window_size // self.block_size; #local_blocks + self.topk = 32 + self.compress_kv = CompressKV(self.num_key_value_heads, self.head_dim,compress_func= compress_type, kernel_size=self.kernel_size,kernel_stride=self.kernel_stride, add_pos_embed=False) + + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + # MiniCPMFlashAttention2 attention does not support output_attentions + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + + # overwrite attention_mask with padding_mask + attention_mask = kwargs.pop("padding_mask") + + output_attentions = False + + bsz, q_len, _ = hidden_states.size() + # assert bsz == 1, '现在只支持batch_size=1' + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + # Flash attention requires the input to have the shape + # batch_size x seq_length x head_dim x hidden_dim + # therefore we just need to keep the original shape + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len) + # if key_states.shape[-2] == 1: #这里是possition ids的问题 + # position_ids = torch.tensor([[kv_seq_len-1]], device=key_states.device, dtype=position_ids.dtype) + # # breakpoint() + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + new_k = key_states + new_v = value_states + new_q = query_states + try: + + past_k , past_v, past_q = past_key_value.__getitem__(self.layer_idx) + except Exception as e: + # If the cache is empty, we need to create a new one + past_k , past_v, past_q = key_states, value_states, query_states + new_k, new_v = None, None + + key_states, value_states ,query_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs,query_states=query_states) + + # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache + # to be able to avoid many of these transpose/reshape/view. + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + dropout_rate = self.attention_dropout if self.training else 0.0 + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in the correct dtype just to be sure everything works as expected. + # This might slowdown training & inference so it is recommended to not cast the LayerNorms + # in fp32. (MiniCPMRMSNorm handles it correctly) + + input_dtype = query_states.dtype + if input_dtype == torch.float32: + # Handle the case where the model is quantized + if hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + if past_key_value is None or new_k is None: + attn_output = self._flash_attention_forward( + query_states, key_states, value_states, attention_mask, position_ids, q_len, dropout=dropout_rate,original_hidden_states=hidden_states,) + else: + # breakpoint() + attn_output = self._flash_attention_forward_with_kv_cache( + query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate,original_hidden_states=hidden_states,past_k=past_k,past_v=past_v,new_k=new_k,new_v=new_v,new_q=new_q) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + def _flash_attention_forward( + self, query_states, key_states, value_states, attention_mask, position_ids, query_length, dropout=0.0, softmax_scale=None,original_hidden_states=None + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`int`, *optional*): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + """ + if not self._flash_attn_uses_top_left_mask: + causal = self.is_causal + else: + # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in MiniCPMFlashAttention2 __init__. + causal = self.is_causal and query_length != 1 + # Contains at least one padding token in the sequence + if attention_mask is not None: + batch_size = query_states.shape[0] + query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( + query_states, key_states, value_states, attention_mask, query_length + ) + original_hidden_states = self._unpad_hidden_states(original_hidden_states, indices_q) + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + attn_output_unpad = self.nsa_forward( + query_states, + key_states, + value_states, + cu_seqlens_q, cu_seqlens_k , + max_seqlen_in_batch_q, max_seqlen_in_batch_k, + original_hidden_states=original_hidden_states + ) + + attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + elif attention_mask is None and position_ids is not None: + batch_size = query_states.size(0) + + query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = ( + prepare_fa2_from_position_ids(query_states, key_states, value_states, position_ids) + ) + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + attn_output_unpad = self.nsa_forward( + query_states, + key_states, + value_states, + cu_seqlens_q, cu_seqlens_k , + max_seqlen_in_batch_q, max_seqlen_in_batch_k, + ) + attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + else: + raise ValueError + + + return attn_output + def _flash_attention_forward_with_kv_cache( + self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None,original_hidden_states=None,past_k=None,past_v=None,new_k=None,new_v=None,new_q=None + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`int`, *optional*): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + """ + if not self._flash_attn_uses_top_left_mask: + causal = self.is_causal + else: + # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in MiniCPMFlashAttention2 __init__. + causal = self.is_causal and query_length != 1 + # Contains at least one padding token in the sequence + if attention_mask is not None: + query_length = query_states.shape[1] + batch_size = query_states.shape[0] + + # query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( + # query_states, key_states, value_states, attention_mask, query_length=query_length + # ) + + #! 这里的attention_mask是没有包括最后一个,所以不准 + assert batch_size == 1, '现在只支持batch_size=1' + query_states = query_states.squeeze(0) + key_states = key_states.squeeze(0) + value_states = value_states.squeeze(0) + original_hidden_states = original_hidden_states.squeeze(0) + cu_seqlens_q=cu_seqlens_k = tensor([0, query_length], device=query_states.device, dtype=torch.int32) + max_seqlen_in_batch_q=max_seqlen_in_batch_k = query_length + attn_output = self.nsa_forward_with_kv_cache( + query_states, + key_states, + value_states, + cu_seqlens_q, cu_seqlens_k , + max_seqlen_in_batch_q, max_seqlen_in_batch_k, + original_hidden_states=original_hidden_states,past_k=past_k,past_v=past_v,new_k=new_k,new_v=new_v,new_q=new_q, batch_size=batch_size ) + # attn_output_unpad = flash_attn_varlen_func( + # query_states, + # key_states, + # value_states, + # cu_seqlens_q=cu_seqlens_q, + # cu_seqlens_k=cu_seqlens_k, + # max_seqlen_q=max_seqlen_in_batch_q, + # max_seqlen_k=max_seqlen_in_batch_k, + # dropout_p=dropout, + # softmax_scale=softmax_scale, + # causal=causal, + # ) + + # attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + else: + attn_output = flash_attn_func( + query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal + ) + + return attn_output + #! -------nsa------- + def nsa_forward(self, + query_layer, + key_layer, + value_layer, + cu_seqlens_q, cu_seqlens_k , + max_seqlen_in_batch_q, max_seqlen_in_batch_k, + original_hidden_states=None + ): + kv = torch.stack((key_layer, value_layer), dim=1) + compressed_k,compressed_v, compressed_cu_seqlens = self.compress_kv(kv, cu_seqlens_k) + compressed_seqlens = compressed_cu_seqlens[1:] - \ + compressed_cu_seqlens[:-1] + compressed_attn_output, topk_idx = compressed_attention( + query_layer, + compressed_k, + compressed_v, + self.kernel_size, + self.kernel_stride, + self.block_size, + self.topk, + cu_seqlens_q, + compressed_cu_seqlens, + max_seqlen_in_batch_q, + compressed_seqlens.max().item(), + None, + init_blocks=self.init_blocks, + local_blocks=self.local_blocks, + ) + + del compressed_k, compressed_v, compressed_cu_seqlens, kv, compressed_seqlens + nheads_k = key_layer.shape[1] + head_mask_type = torch.tensor([1] * nheads_k, device=query_layer.device, dtype=torch.int32) + streaming_info = torch.tensor([0, 0] * nheads_k, device=query_layer.device, dtype=torch.int32) + exact_streaming =False + + repeat_times = 1 + if repeat_times > 1: + query_layer_repeat = query_layer.repeat_interleave(repeat_times, dim=-2) + else: + query_layer_repeat = query_layer + topk_attn_output = block_sparse_attn_func( + query_layer_repeat, + key_layer, + value_layer, + cu_seqlens_q, + cu_seqlens_k, + head_mask_type, + streaming_info, + topk_idx, + max_seqlen_in_batch_q, max_seqlen_in_batch_k, + self.attention_dropout, + deterministic=False, + softmax_scale=None, + is_causal=True, + exact_streaming=False, + return_attn_probs=False, + block_window_size=self.window_size // self.block_size, + use_checkpoint=False, + ) + # import pdb; pdb.set_trace() + # raise ValueError('debug') + if repeat_times > 1: + topk_attn_output = topk_attn_output.view(topk_attn_output.shape[0],topk_attn_output.shape[1]//repeat_times,repeat_times,-1).mean(dim=-2) + return topk_attn_output + + #! -------nsa------- + def nsa_forward_with_kv_cache(self, + query_layer, + key_layer, + value_layer, + cu_seqlens_q, cu_seqlens_k , + max_seqlen_in_batch_q, max_seqlen_in_batch_k, + original_hidden_states=None,past_k=None,past_v=None,new_k=None,new_v=None,new_q=None, batch_size=None, + ): + # breakpoint() + kv = torch.stack((key_layer, value_layer), dim=1) + compressed_k,compressed_v, compressed_cu_seqlens = self.compress_kv(kv, cu_seqlens_k) + compressed_seqlens = compressed_cu_seqlens[1:] - \ + compressed_cu_seqlens[:-1] + compressed_attn_output, topk_idx = compressed_attention( + query_layer, + compressed_k, + compressed_v, + self.kernel_size, + self.kernel_stride, + self.block_size, + self.topk, + cu_seqlens_q, + compressed_cu_seqlens, + max_seqlen_in_batch_q, + compressed_seqlens.max().item(), + None, + init_blocks=self.init_blocks, + local_blocks=self.local_blocks, + ) + compressed_attn_output = compressed_attn_output[-1].unsqueeze(0).unsqueeze(0) + + del compressed_k, compressed_v, compressed_cu_seqlens, kv, compressed_seqlens + nheads_k = key_layer.shape[1] + head_mask_type = torch.tensor([1] * nheads_k, device=query_layer.device, dtype=torch.int32) + streaming_info = torch.tensor([0, 0] * nheads_k, device=query_layer.device, dtype=torch.int32) + exact_streaming =False + + repeat_times = 1 + past_k = past_k.transpose(1, 2).contiguous() + past_v = past_v.transpose(1, 2).contiguous() + if new_k is not None: + + new_k = new_k.transpose(1, 2).contiguous() + if new_v is not None: + new_v = new_v.transpose(1, 2).contiguous() + new_q = new_q.transpose(1, 2).contiguous() + if repeat_times > 1: + new_q = new_q.repeat_interleave(repeat_times, dim=-2) + else: + new_q = new_q + #! 暂时 + # assert batch_size == 1, '只支持batch_size =1' + + + cache_batch_idx = torch.arange(batch_size, device=query_layer.device, dtype=torch.int32) + current_seqlens_k = cu_seqlens_k[1:] - cu_seqlens_k[:-1] + new_topk_idx = [] + if new_k is not None: + for i in range(batch_size): + new_topk_idx.append(topk_idx[:,current_seqlens_k[i]-1,:].unsqueeze(1)) + topk_idx = torch.stack(new_topk_idx, dim=0) + else: + # prefilling + for i in range(batch_size): + if i == 0: + start = 0 + else: + start = current_seqlens_k[i-1] + new_topk_idx.append(topk_idx[:,start:current_seqlens_k[i],:]) + topk_idx = torch.stack(new_topk_idx, dim=0) + seqlen_k=key_layer.shape[0] #! 只考虑单个batch + seqlens_k = torch.full((batch_size,), seqlen_k - 1, dtype=torch.int32, device=new_q.device) + + + past_k = torch.cat([past_k, torch.zeros_like(new_k,dtype=new_k.dtype)], dim=1) #填充多一个 + past_v = torch.cat([past_v, torch.zeros_like(new_v,dtype=new_v.dtype)], dim=1) #填充多一个 + topk_attn_output, softmax_lse = block_sparse_attn_kvcache_func( + q=new_q, # [batch_size, seqlen_q, nheads, d] + k_cache=past_k, # [batch_size, max_seqlen_k, nheads_k, d] + v_cache=past_v, # [batch_size, max_seqlen_k, nheads_k, d] + m_block_dim=16, + n_block_dim=64, + head_mask_type=head_mask_type, + streaming_info=None,#streaming_info, + topk_idx=topk_idx, + k=new_k, # [batch_size, 1, nheads_k, d] + v=new_v, # [batch_size, 1, nheads_k, d] + seqlens_k= seqlens_k,#current_seqlens_k-1 ,#! 这边要对齐kv cahce的长度, # Current positions in cache + rotary_cos=None, # No rotary embeddings + rotary_sin=None, # No rotary embeddings + cache_batch_idx=cache_batch_idx, + alibi_slopes=None, + softmax_scale=None, + causal=False, # Renaming to match function signature + exact_streaming=exact_streaming, + window_size_left=-1, # Using individual parameters instead of tuple + window_size_right=-1, + block_window_size=self.window_size // self.block_size, + rotary_interleaved=False, + num_splits=16, + # num_topk=self.topk, + ) + + if repeat_times > 1: + topk_attn_output = topk_attn_output.view(topk_attn_output.shape[0],topk_attn_output.shape[1],topk_attn_output.shape[2]//repeat_times,repeat_times,-1).mean(dim=-2) + return topk_attn_output + + + def _unpad_hidden_states(self, hidden_states,indices): + # Unpad the hidden states using the indices + batch_size, seq_len, hidden_dim = hidden_states.shape + if seq_len ==1: + return hidden_states.reshape(batch_size , -1, self.head_dim) + hidden_states = index_first_axis( + hidden_states.reshape(batch_size * seq_len, -1, self.head_dim), indices + ) + return hidden_states + def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape + + key_layer = index_first_axis( + key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k + ) + value_layer = index_first_axis( + value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k + ) + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +class MiniCPMSdpaAttention(MiniCPMAttention): + """ + MiniCPM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `MiniCPMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from MiniCPMAttention.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "MiniCPMModel is using MiniCPMSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " + 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + + # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query_states.device.type == "cuda" and attention_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=attention_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. + is_causal=self.is_causal and attention_mask is None and q_len > 1, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value + + +MINICPM_ATTENTION_CLASSES = { + "eager": MiniCPMAttention, + "flash_attention_2": MiniCPMFlashAttention2, + "sdpa": MiniCPMSdpaAttention, +} + + +class MiniCPMDecoderLayer(nn.Module): + def __init__(self, config: MiniCPMConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = MINICPM_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) + + self.mlp = MiniCPMMLP(config) + self.input_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.scale_depth = config.scale_depth + self.num_hidden_layers = config.num_hidden_layers + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + **kwargs, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): + attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, + query_sequence_length, key_sequence_length)` if default attention is used. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers)) + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers)) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +MINICPM_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`MiniCPMConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare MiniCPM Model outputting raw hidden-states without any specific head on top.", + MINICPM_START_DOCSTRING, +) +class MiniCPMPreTrainedModel(PreTrainedModel): + config_class = MiniCPMConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["MiniCPMDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_cache_class = True + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +MINICPM_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare MiniCPM Model outputting raw hidden-states without any specific head on top.", + MINICPM_START_DOCSTRING, +) +class MiniCPMModel(MiniCPMPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`] + + Args: + config: MiniCPMConfig + """ + + def __init__(self, config: MiniCPMConfig): + super().__init__(config) + assert config._attn_implementation == "flash_attention_2", "Only flash_attention_2 is supported for hybrid attention" + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [MiniCPMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self._use_sdpa = config._attn_implementation == "sdpa" + self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" + + self.norm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = True + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape[:2] + elif inputs_embeds is not None: + batch_size, seq_length = inputs_embeds.shape[:2] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + past_key_values_length = 0 + globals()['token_now'] +=1 + if use_cache: + # breakpoint() + use_legacy_cache = not isinstance(past_key_values, Cache) + if use_legacy_cache: + raise ValueError( + "必须使用新的past_key_values格式, 例如Cache类, 而不是旧的tuple格式." + ) + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + # 添加新的query_cache + past_key_values_length = past_key_values.get_usable_length(seq_length) + if past_key_values_length == 0: + past_key_values = DynamicCacheQKV() + else: + assert isinstance(past_key_values, DynamicCacheQKV), "past_key_values must be a DynamicCacheQKV instance" + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) * self.config.scale_emb + + # if self._use_flash_attention_2: + # # 2d mask is passed through the layers + # # attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None + # if attention_mask is None: + # raise ValueError( + # f"需要attention_mask for flash attention, but got {attention_mask}." + # ) + elif self._use_sdpa and not output_attentions: + # output_attentions=True can not be supported when using SDPA, and we fall back on + # the manual implementation that requires a 4D causal mask in all cases. + attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + ) + else: + # 4d mask is passed through the layers + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length + ) + + # embed positions + hidden_states = inputs_embeds + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = None + if use_cache: + next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +class MiniCPMForCausalLM(MiniCPMPreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = MiniCPMModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, MiniCPMForCausalLM + + >>> model = MiniCPMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + if self.config.pretraining_tp > 1: + lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0) + logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)] + logits = torch.cat(logits, dim=-1) + else: + logits = self.lm_head(hidden_states / (self.config.hidden_size / self.config.dim_model_base)) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + if past_key_values is not None: + if isinstance(past_key_values, Cache): + cache_length = past_key_values.get_seq_length() + past_length = past_key_values.seen_tokens + max_cache_length = None # past_key_values.get_max_length() #! 换成max, 因为如果不是max的话,会裁剪attentio mask + else: + cache_length = past_length = past_key_values[0][0].shape[2] + max_cache_length = None + + # Keep only the unprocessed tokens: + # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where + # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as + # input) + if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: + input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] + # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard + # input_ids based on the past_length. + elif past_length < input_ids.shape[1]: + input_ids = input_ids[:, past_length:] + # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. + + # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. + if ( + max_cache_length is not None + and attention_mask is not None + and cache_length + input_ids.shape[1] > max_cache_length + ): + attention_mask = attention_mask[:, -max_cache_length:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -input_ids.shape[1] :] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past + + @torch.inference_mode() + def chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = "user", + max_length: int = 4096, num_beams=1, do_sample=True, top_p=0.8, temperature=0.3, logits_processor=None, + **kwargs): + if history is None: + history = [] + if logits_processor: + gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + else: + gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + + history.append({"role": role, "content": query}) + history_str = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=False) + inputs = tokenizer(history_str, return_tensors='pt').to(self.device) + outputs = self.generate(**inputs, **gen_kwargs) + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1] + response = tokenizer.decode(outputs) + pattern = re.compile(r".*?(?=|<用户>)", re.DOTALL) + matches = pattern.findall(response) + if len(matches) > 0: + response = matches[0] + history.append({"role": "assistant", "content": response}) + return response, history + + +@add_start_docstrings( + """ + The MiniCPM Model transformer with a sequence classification head on top (linear layer). + + [`MiniCPMForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + MINICPM_START_DOCSTRING, +) +class MiniCPMForSequenceClassification(MiniCPMPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = MiniCPMModel(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to( + logits.device + ) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000..0200b85 --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,40 @@ +{ + "additional_special_tokens": [ + "<|im_end|>", + "<|im_start|>", + "<|tool_call|>", + "<|execute_start|>", + "<|execute_end|>", + "<|fim_prefix|>", + "<|fim_middle|>", + "<|fim_suffix|>" + ], + "bos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "<|im_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": { + "content": "<|im_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "unk_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + } +} diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..d433aed --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:adf7208af154a5ca065d2eda4e5419e02aac58c2c00627874748b75ec6769094 +size 6701371 diff --git a/tokenizer.model b/tokenizer.model new file mode 100644 index 0000000..3acef16 --- /dev/null +++ b/tokenizer.model @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bb74d51116831c3bf65db812c553f94ab0c88dcf97a5bbb37e3504f6d359c530 +size 1181204 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..218e56e --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,120 @@ +{ + "add_bos_token": true, + "add_eos_token": false, + "add_prefix_space": null, + "added_tokens_decoder": { + "0": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "1": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "2": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73440": { + "content": "<|im_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73441": { + "content": "<|im_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73442": { + "content": "<|tool_call|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73443": { + "content": "<|execute_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73444": { + "content": "<|execute_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73445": { + "content": "<|fim_prefix|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73446": { + "content": "<|fim_middle|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73447": { + "content": "<|fim_suffix|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "additional_special_tokens": [ + "<|im_end|>", + "<|im_start|>", + "<|tool_call|>", + "<|execute_start|>", + "<|execute_end|>", + "<|fim_prefix|>", + "<|fim_middle|>", + "<|fim_suffix|>" + ], + "bos_token": "", + "chat_template": "{%- macro json_to_python_type(param_name, json_spec) %}\n{%- set basic_type_map = {\n 'string': 'str',\n 'number': 'float',\n 'integer': 'int',\n 'boolean': 'bool',\n 'null': 'None'\n} %}\n\n{%- if json_spec.enum %}\n {{- param_name|title }}\n{%- elif basic_type_map[json_spec.type] is defined %}\n {{- basic_type_map[json_spec.type] }}\n{%- elif json_spec.type == 'array' %}\n {{- 'List[' + json_to_python_type(param_name, json_spec['items']) + ']' }}\n{%- elif json_spec.type == 'object' %}\n {{- 'Dict[str, ' + json_to_python_type(param_name, json_spec.additionalProperties if json_spec.additionalProperties else 'Any') + ']' if not json_spec.properties else param_name|title }}\n{%- elif json_spec.type is iterable %}\n {{- 'Union[' }}\n {%- for t in json_spec.type %}\n {{- json_to_python_type(param_name, {'type': t}) }}\n {{- ', ' if not loop.last }}\n {%- endfor %}\n {{- ']' }}\n{%- else %}\n {{- 'Any' }}\n{%- endif %}\n{%- endmacro %}\n\n{%- macro object_to_fields(json_spec, field_indent) %}\n {%- set o_ns = namespace(f = caller()) %}\n {%- for param_name, param_fields in json_spec.properties|items %}\n {%- if param_fields.enum %}\n {{- '\\n\\nclass ' + param_name|title + '(Enum):\\n' }}\n {%- for enum_option in param_fields.enum %}\n {{- ' enum_' + loop.index0|string + ' = ' + enum_option|tojson + '\\n' }}\n {%- endfor %}\n {%- elif param_fields.type == 'object' and param_fields.properties %}\n {%- call object_to_fields(param_fields, ' ') %}\n {{- '\\n\\nclass ' + param_name|title + '(BaseModel):\\n' }}\n {%- endcall %}\n {%- elif param_fields.type == 'array' and param_fields['items'] and param_fields['items'].type == 'object' and param_fields['items'].properties %}\n {%- call object_to_fields(param_fields['items'], ' ') %}\n {{- '\\n\\nclass ' + param_name|title + '(BaseModel):\\n' }}\n {%- endcall %}\n {%- endif %}\n {%- set param_default = param_fields.default|tojson if param_fields.default is string else param_fields.default|string if param_fields.default is defined else 'None' %}\n {%- set o_ns.f = o_ns.f + field_indent + param_name + ': ' %}\n {%- set o_ns.f = o_ns.f + ('Optional[' + json_to_python_type(param_name, param_fields) + ']' if param_name not in json_spec.required else json_to_python_type(param_name, param_fields)) %}\n {%- if not param_fields.title and not param_fields.description and not param_fields.pattern %}\n {%- set o_ns.f = o_ns.f + (' = ' + param_default if param_name not in json_spec.required else '') %}\n {%- else %}\n {%- set o_ns.f = o_ns.f + (' = Field(...' if param_name in json_spec.required else ' = Field(' + param_default) %}\n {%- set o_ns.f = o_ns.f + (', description=' + param_fields.description|tojson if param_fields.description else '') %}\n {%- set o_ns.f = o_ns.f + (', regex=' + param_fields.pattern|tojson if param_fields.pattern else '') %}\n {%- set o_ns.f = o_ns.f + (', title=' + param_fields.title|tojson if param_fields.title else '') %}\n {%- set o_ns.f = o_ns.f + ')' %}\n {%- endif %}\n {%- set o_ns.f = o_ns.f + '\\n' %}\n {%- endfor %}\n {{- o_ns.f }}\n{%- endmacro %}\n\n{%- macro tool_parser(tools) %}\n{%- for tool in tools %}\n {%- if tool.type is not defined or tool.type == 'function' %}\n {%- if tool.function is defined %}\n {%- set tool = tool.function %}\n {%- endif %}\n {%- set tool_params = tool.parameters if tool.parameters is defined else none %}\n {%- call object_to_fields(tool_params, ' ') %}\n {{- '\\n\\ndef ' + tool.name + '(' }}\n {%- if tool_params %}\n {%- for param_name, param_fields in tool_params.properties|items %}\n {%- set param_default = param_fields.default|tojson if param_fields.default is string else param_fields.default|string if param_fields.default is defined else 'None' %}\n {{- ', ' if loop.index0 != 0 }}\n {{- param_name }}\n {{- '=' + param_default if param_name not in tool_params.required }}\n {%- endfor %}\n {%- endif %}\n {{- '):\\n \"\"\"' }}\n {{- tool.description }}\n {{- '\\n\\n Args:\\n' if tool_params else '\\n' }}\n {%- endcall %}\n {{- ' \"\"\"\\n' }}\n {%- endif %}\n{%- endfor %}\n{%- endmacro %}\n\n{%- if messages[0]['role'] == 'system' %}\n {%- set loop_messages = messages[1:] %}\n {%- set system_message = messages[0]['content'] %}\n{%- else %}\n {%- set loop_messages = messages %}\n {%- set system_message = '' %}\n{%- endif %}\n{{- '<|im_start|>system\\n' + system_message if system_message or tools }}\n{%- if tools %}\n {{- '\\n# Functions\\nHere is a list of functions that you can invoke:\\n```python\\nfrom enum import Enum\\nfrom typing import List, Dict, Optional\\nfrom pydantic import BaseModel, Field\\n\\n' }}\n {{- tool_parser(tools) }}\n {{- \"\\n```\\n\\n# Function Call Rule and Output Format\\n- If the user's question can be answered without calling any function, please answer the user's question directly. In this situation, you should return your thought and answer the user's question directly.\\n- If the user cannot be answered without calling any function, and the user does not provide enough information to call functions, please ask the user for more information. In this situation, you should return your thought and ask the user for more information.\\n- If the user's question cannot be answered without calling any function, and the user has provided enough information to call functions to solve it, you should call the functions. In this situation, the assistant should return your thought and call the functions.\\n- Use default parameters unless the user has specified otherwise.\\n- You should answer in the following format:\\n\\n<|thought_start|>\\n{explain why the user's question can be answered without calling a function or why you should ask the user for more information or why you should call one or more functions and your plan to solve the user's question.}\\n<|thought_end|>\\n<|tool_call_start|>\\n```python\\nfunc1(params_name=params_value, params_name2=params_value2...)\\nfunc2(params)\\n```\\n<|tool_call_end|>\\n{answer the user's question directly or ask the user for more information}\" }}\n{%- endif %}\n{{- '<|im_end|>\\n' if system_message or tools }}\n{%- for message in loop_messages %}\n {%- set content = message.content %}\n {%- if message.role == 'assistant' and message.tool_calls %}\n {{- '<|im_start|>' + message.role + '\\n' }}\n {{- '<|thought_start|>\\n' + message.thought + '\\n<|thought_end|>\\n' if message.thought }}\n {{- '<|tool_call_start|>\\n```python\\n' }}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- tool_call.name + '(' }}\n {%- if tool_call.arguments is defined and tool_call.arguments|length > 0 %}\n {%- for param_name, param_value in tool_call.arguments|items %}\n {{- param_name + '=' + param_value|tojson }}\n {{- ',' if not loop.last }}\n {%- endfor %}\n {%- endif %}\n {{- ')\\n' }}\n {%- endfor %}\n {{- '```\\n<|tool_call_end|>\\n' }}\n {{- content if content and not content.startswith('<|tool_call_start|>') }}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == 'assistant' and message.thought %}\n {{- '<|im_start|>' + message.role + '\\n' + '<|thought_start|>\\n' + message.thought + '\\n<|thought_end|>\\n' + content + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endfor %}\n\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}", + "clean_up_tokenization_spaces": false, + "eos_token": "<|im_end|>", + "extra_special_tokens": {}, + "legacy": true, + "model_max_length": 1000000000000000019884624838656, + "pad_token": "<|im_end|>", + "padding_side": "left", + "sp_model_kwargs": {}, + "spaces_between_special_tokens": false, + "split_special_tokens": false, + "tokenizer_class": "LlamaTokenizer", + "unk_token": "", + "use_default_system_prompt": false +}