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pszemrajPeter Szemraj
pszemraj
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Peter Szemraj
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fix imports, simplify RMSNorm (#1)
* fix recursive imports Signed-off-by: Peter Szemraj <peterszemraj+dev@gmail.com> * torch-only norms Signed-off-by: Peter Szemraj <peterszemraj+dev@gmail.com> * 📝 install tips Signed-off-by: Peter Szemraj <peterszemraj+dev@gmail.com> --------- Signed-off-by: Peter Szemraj <peterszemraj+dev@gmail.com> Co-authored-by: Peter Szemraj <peterszemraj+dev@gmail.com>
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‎README.md‎

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This aims to be a simpler implementation of the [original repo](https://github.com/microsoft/Samba).
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## Installation
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> [!TIP]
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> While the `pip install` command _should_ install all deps and the package, in practice some of the more CUDA-heavy deps are better installed separately from source. See section below for more details.
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```bash
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git clone https://github.com/pszemraj/samba-pytorch.git
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cd samba-pytorch
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pip install -e .
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```
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### Installing custom kernel packages first
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After installing `torch`, `xformers`, and `flash-attn`, you may want to install `mamba-ssm`, `causal-conv1d`, and `fla` from source:
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```bash
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pip install --upgrade pip ninja
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pip install git+https://github.com/state-spaces/mamba.git --no-build-isolation
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pip install git+https://github.com/Dao-AILab/causal-conv1d.git --no-build-isolation
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pip install git+https://github.com/sustcsonglin/flash-linear-attention@98c176e --no-build-isolation
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```
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Then, clone this repo and run commands as above.
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## Usage
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A basic example of creating a random model from a named config:
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```python
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from samba_pytorch import Config, GPT
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cfg = Config.from_name('Samba_421M_1k_window')
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print*(cfg)
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model = GPT(cfg)
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model
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```
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## repo structure
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```text

‎samba_pytorch/config.py‎

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# Copyright Lightning AI. Licensed under the Apache License 2.0,
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# see LICENSE file at https://github.com/Lightning-AI/litgpt/blob/main/LICENSE
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import warnings
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from dataclasses import dataclass
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from typing import Any, Literal, Optional, Type
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import torch
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from typing_extensions import Self
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import samba_pytorch.samba
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from samba_pytorch.utils import find_multiple
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@property
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def mlp_class(self) -> Type:
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from samba_pytorch import samba
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# `self._mlp_class` cannot be the type to keep the config json serializable
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return getattr(samba_pytorch.samba, self._mlp_class)
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return getattr(samba, self._mlp_class)
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@property
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def norm_class(self) -> Type:
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return RMSNorm
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elif self._norm_class == "FusedRMSNorm":
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from samba_pytorch.modules.rmsnorm import FusedRMSNorm
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warnings.warn(
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"FusedRMSNorm has been removed, using standard torch RMSNorm instead"
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)
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from samba_pytorch.modules.rmsnorm import RMSNorm
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return FusedRMSNorm
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return RMSNorm
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return getattr(torch.nn, self._norm_class)
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rotary_percentage=1.0,
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parallel_residual=False,
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bias=False,
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_norm_class="FusedRMSNorm",
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_norm_class="RMSNorm",
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norm_eps=1e-5,
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_mlp_class="LLaMAMLP",
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intermediate_size=4096,
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rotary_percentage=1.0,
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parallel_residual=False,
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bias=False,
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_norm_class="FusedRMSNorm",
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_norm_class="RMSNorm",
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norm_eps=1e-5,
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_mlp_class="LLaMAMLP",
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intermediate_size=4096,
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rotary_percentage=1.0,
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parallel_residual=False,
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bias=False,
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_norm_class="FusedRMSNorm",
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_norm_class="RMSNorm",
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norm_eps=1e-5,
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full_per_layer=2,
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_mlp_class="LLaMAMLP",
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rotary_percentage=1.0,
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parallel_residual=False,
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bias=False,
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_norm_class="FusedRMSNorm",
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_norm_class="RMSNorm",
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_mlp_class="LLaMAMLP",
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intermediate_size=4608,
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rotary_percentage=1.0,
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_norm_class="FusedRMSNorm",
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_norm_class="RMSNorm",
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norm_eps=1e-5,
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intermediate_size=4608,
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parallel_residual=False,
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_norm_class="FusedRMSNorm",
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_norm_class="RMSNorm",
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parallel_residual=False,
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_norm_class="FusedRMSNorm",
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_norm_class="RMSNorm",
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_norm_class="FusedRMSNorm",
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_norm_class="RMSNorm",
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_norm_class="FusedRMSNorm",
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_norm_class="FusedRMSNorm",
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_norm_class="FusedRMSNorm",
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parallel_residual=True,
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