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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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 *tfevents* filter=lfs diff=lfs merge=lfs -text
+bg.png filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+datasets:
+  - HuggingFaceFW/fineweb-edu
+  - openbmb/Ultra-FineWeb
+  - HuggingFaceTB/finemath
+  - HuggingFaceTB/smollm-corpus
+  - openbmb/UltraData-Math
+language:
+  - en
+library_name: transformers
+tags:
+  - causal-lm
+  - decoder-only
+  - grouped-query-attention
+  - rope
+  - swiglu
+  - custom-tokenizer
+  - curriculum-learning
+  - xsa
+pipeline_tag: text-generation
+---
+
+![bg](bg.png)
+# Atom 3.4m
+
+Atom is a 3.4M parameter causal language model developed by **Universal Computing Research**. It was pretrained from scratch as a compact research model for studying language-model architecture, data curricula, and small-model benchmarking.
+
+## Model details
+
+- Architecture: causal decoder-only language model
+- Parameters: 3,412,800
+- Layers: 7
+- Hidden size: 192
+- Attention: 3 query heads and 1 key-value head (grouped-query attention)
+- Head dimension: 64
+- Feed-forward size: 480
+- Context length: 512 tokens
+- Positional encoding: rotary position embeddings (RoPE)
+- RoPE Theta = 5000.0
+- Normalization: RMSNorm
+- Activation: gated SiLU feed-forward network
+- Vocabulary size: 4,096 tokens
+- Tokenizer: custom byte-level BPE, exposed as `GPT2TokenizerFast`
+- Training tokens: approximately 5 billion 
+- License: Apache-2.0
+
+The model uses tied input and output embeddings. Its custom attention implementation combines grouped-query attention with XSE.
+
+## Tokenizer
+
+Atom uses a custom byte-level BPE tokenizer trained specifically for this pretraining corpus. The tokenizer has a vocabulary of 4,096 tokens and includes dedicated padding, beginning-of-sequence, end-of-sequence, unknown, and end-of-text tokens.
+
+## Training data and curriculum
+
+Atom was trained on a curriculum combining general web text, educational material, synthetic textbook-style content, and mathematical data. The mixture changed gradually during training: general web data was emphasized earlier, while educational, synthetic, and mathematical material received more weight later.
+
+Approximate proportions over the complete training run were:
+
+| Dataset | Subset / split used | Approximate proportion |
+|---|---|---:|
+| [HuggingFaceFW/fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) | All available `CC-MAIN-*` configurations under `data/`, `train` split | 39% |
+| [openbmb/Ultra-FineWeb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb) | English v1.4 (`ultrafineweb_en_v1_4`; `en` split) | 31% |
+| [HuggingFaceTB/finemath](https://huggingface.co/datasets/HuggingFaceTB/finemath) | `finemath-3plus`, `train` split | 12% |
+| [HuggingFaceTB/smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | `cosmopedia-v2`, `train` split | 12% |
+| [openbmb/UltraData-Math](https://huggingface.co/datasets/openbmb/UltraData-Math) | `UltraData-Math-L2-preview`, `train` split | 6% |
+
+These percentages describe the approximate aggregate sampling mixture rather than exact document counts. Refer to the individual dataset cards for their source information, licenses, and usage conditions.
+
+## Intended use
+
+This is a small base language model intended for research and benchmarking. It may be useful for experiments involving compact architectures, pretraining curricula, tokenization, evaluation pipelines, and resource-constrained inference.
+
+Atom is a base model and has not been instruction-tuned or aligned for assistant-style interaction.
+
+## Evaluation
+
+Atom was evaluated with EleutherAI's `lm-evaluation-harness` and ArithMark-2.0.
+
+### lm-evaluation-harness
+
+| Task | Metric | Score |
+|---|---|---:|
+| ARC-Easy | `acc_norm` | 33.08% |
+| ARC-Challenge | `acc_norm` | 21.76% |
+| HellaSwag | `acc_norm` | 27.65% |
+| PIQA | `acc_norm` | 55.71% |
+
+### ArithMark-2.0
+
+| Benchmark | Metric | Score |
+|---|---|---:|
+| ArithMark-2.0 | `acc` | 27.36% |
+
+**Average score: 34.54%**
+
+
+## Limitations
+
+Atom is a very small model and should not be expected to produce reliable factual, safety-critical, or instruction-following outputs. Its short context window and limited capacity constrain coherence, knowledge recall, reasoning, and long-form generation.
+
+The model may reproduce errors, biases, or undesirable patterns present in its training data. It has not undergone dedicated safety training and should not be used for high-stakes decisions.

config.json

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+{
+  "architectures": [
+    "GPTForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "config.GPTConfig",
+    "AutoModelForCausalLM": "model.GPTForCausalLM"
+  },
+  "block_size": 512,
+  "dtype": "float32",
+  "head_dim": 64,
+  "hidden_size": 192,
+  "intermediate_size": 480,
+  "labels_are_shifted": true,
+  "max_position_embeddings": 512,
+  "model_type": "gpt",
+  "num_attention_heads": 3,
+  "num_hidden_layers": 7,
+  "num_key_value_heads": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 5000.0,
+  "transformers_version": "4.57.6",
+  "vocab_size": 4096,
+  "xsa_projection": true
+}

config.py

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+"""Environment-driven training configuration."""
+
+from __future__ import annotations
+
+import os
+import math
+import uuid
+from dataclasses import dataclass, field
+from pathlib import Path
+
+from transformers import PretrainedConfig
+
+
+DEFAULT_VOCAB_SIZE = 4096
+DEFAULT_HIDDEN_SIZE = 192
+DEFAULT_NUM_HIDDEN_LAYERS = 7
+DEFAULT_NUM_ATTENTION_HEADS = 3
+DEFAULT_NUM_KEY_VALUE_HEADS = 1
+DEFAULT_HEAD_DIM = DEFAULT_HIDDEN_SIZE // DEFAULT_NUM_ATTENTION_HEADS
+DEFAULT_INTERMEDIATE_SIZE = DEFAULT_HIDDEN_SIZE * 5 // 2
+DEFAULT_BLOCK_SIZE = 512
+DEFAULT_ROPE_THETA = 5000.0
+
+
+class GPTConfig(PretrainedConfig):
+    model_type = "gpt"
+
+    def __init__(
+        self,
+        vocab_size: int = DEFAULT_VOCAB_SIZE,
+        hidden_size: int = DEFAULT_HIDDEN_SIZE,
+        num_hidden_layers: int = DEFAULT_NUM_HIDDEN_LAYERS,
+        num_attention_heads: int = DEFAULT_NUM_ATTENTION_HEADS,
+        num_key_value_heads: int | None = DEFAULT_NUM_KEY_VALUE_HEADS,
+        intermediate_size: int | None = DEFAULT_INTERMEDIATE_SIZE,
+        head_dim: int | None = None,
+        block_size: int = DEFAULT_BLOCK_SIZE,
+        rope_theta: float = DEFAULT_ROPE_THETA,
+        rms_norm_eps: float = 1e-6,
+        xsa_projection: bool = True,
+        tie_word_embeddings: bool = True,
+        labels_are_shifted: bool = False,
+        **kwargs,
+    ):
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+        if head_dim is None:
+            if hidden_size % num_attention_heads != 0:
+                raise ValueError("hidden_size must be divisible by num_attention_heads")
+            head_dim = hidden_size // num_attention_heads
+        if intermediate_size is None:
+            intermediate_size = hidden_size * 4
+        if num_attention_heads % num_key_value_heads != 0:
+            raise ValueError("num_attention_heads must be divisible by num_key_value_heads")
+        if head_dim % 2 != 0:
+            raise ValueError("head_dim must be even for RoPE")
+
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+        self.vocab_size = int(vocab_size)
+        self.hidden_size = int(hidden_size)
+        self.num_hidden_layers = int(num_hidden_layers)
+        self.num_attention_heads = int(num_attention_heads)
+        self.num_key_value_heads = int(num_key_value_heads)
+        self.intermediate_size = int(intermediate_size)
+        self.head_dim = int(head_dim)
+        self.block_size = int(block_size)
+        self.max_position_embeddings = int(block_size)
+        self.rope_theta = float(rope_theta)
+        self.rms_norm_eps = float(rms_norm_eps)
+        self.xsa_projection = bool(xsa_projection)
+        self.labels_are_shifted = bool(labels_are_shifted)
+
+
+def _bool_env(name: str, default: bool) -> bool:
+    raw = os.environ.get(name)
+    if raw is None:
+        return default
+    return raw.strip().lower() in {"1", "true", "yes", "on"}
+
+
+def _path_env(name: str, default: str) -> str:
+    return str(Path(os.environ.get(name, default)).expanduser())
+
+
+@dataclass
+class Hyperparameters:
+    data_dir: str = field(default_factory=lambda: _path_env("DATA_DIR", "."))
+    tokenized_dir: str = field(default_factory=lambda: _path_env("TOKENIZED_DIR", "tokenized"))
+    tokenizer_dir: str = field(default_factory=lambda: _path_env("TOKENIZER_DIR", "tokenizer_4k"))
+    tokenizer_path: str = field(default_factory=lambda: os.environ.get("TOKENIZER_PATH", ""))
+    curriculum_path: str = field(default_factory=lambda: os.environ.get("CURRICULUM_PATH", ""))
+    mix_weights_path: str = field(default_factory=lambda: os.environ.get("MIX_WEIGHTS_PATH", ""))
+    run_id: str = field(default_factory=lambda: os.environ.get("RUN_ID", str(uuid.uuid4())))
+    seed: int = field(default_factory=lambda: int(os.environ.get("SEED", "1337")))
+    rank: int = field(init=False)
+
+    iterations: int = field(default_factory=lambda: int(os.environ.get("ITERATIONS", "10000")))
+    requested_train_tokens: int = field(init=False)
+    train_tokens: int = field(init=False)
+    decay_start_frac: float = field(default_factory=lambda: float(os.environ.get("DECAY_START_FRAC", "0.7")))
+    warmup_steps: int = field(default_factory=lambda: int(os.environ.get("WARMUP_STEPS", "0")))
+    lr_warmup_steps: int = field(default_factory=lambda: int(os.environ.get("LR_WARMUP_STEPS", "500")))
+    train_batch_tokens: int = field(default_factory=lambda: int(os.environ.get("TRAIN_BATCH_TOKENS", str(DEFAULT_BLOCK_SIZE * 512))))
+    train_seq_len: int = field(init=False)
+    eval_seq_len: int = field(init=False)
+    grad_accum_steps: int = field(default_factory=lambda: int(os.environ.get("GRAD_ACCUM_STEPS", "2")))
+    train_log_every: int = field(default_factory=lambda: int(os.environ.get("TRAIN_LOG_EVERY", "100")))
+    train_log_first_steps: int = field(default_factory=lambda: int(os.environ.get("TRAIN_LOG_FIRST_STEPS", "500")))
+
+    val_batch_tokens: int = field(default_factory=lambda: int(os.environ.get("VAL_BATCH_TOKENS", str(DEFAULT_BLOCK_SIZE * 256))))
+    val_loss_every: int = field(default_factory=lambda: int(os.environ.get("VAL_LOSS_EVERY", "1000")))
+    val_max_tokens: int = field(default_factory=lambda: int(os.environ.get("VAL_MAX_TOKENS", "10_000_000")))
+
+    vocab_size: int = field(default_factory=lambda: int(os.environ.get("VOCAB_SIZE", str(DEFAULT_VOCAB_SIZE))))
+    hidden_size: int = field(default_factory=lambda: int(os.environ.get("HIDDEN_SIZE", os.environ.get("MODEL_DIM", str(DEFAULT_HIDDEN_SIZE)))))
+    num_hidden_layers: int = field(default_factory=lambda: int(os.environ.get("NUM_HIDDEN_LAYERS", os.environ.get("NUM_LAYERS", str(DEFAULT_NUM_HIDDEN_LAYERS)))))
+    num_attention_heads: int = field(default_factory=lambda: int(os.environ.get("NUM_ATTENTION_HEADS", os.environ.get("NUM_HEADS", str(DEFAULT_NUM_ATTENTION_HEADS)))))
+    num_key_value_heads: int = field(default_factory=lambda: int(os.environ.get("NUM_KEY_VALUE_HEADS", os.environ.get("NUM_KV_HEADS", str(DEFAULT_NUM_KEY_VALUE_HEADS)))))
+    head_dim: int = field(init=False)
+    intermediate_size: int = field(default_factory=lambda: int(os.environ.get("INTERMEDIATE_SIZE", os.environ.get("INTERMEDIATE", str(DEFAULT_INTERMEDIATE_SIZE)))))
+    block_size: int = field(default_factory=lambda: int(os.environ.get("BLOCK_SIZE", str(DEFAULT_BLOCK_SIZE))))
+    rope_theta: float = field(default_factory=lambda: float(os.environ.get("ROPE_THETA", os.environ.get("ROPE_BASE", str(DEFAULT_ROPE_THETA)))))
+    rms_norm_eps: float = field(default_factory=lambda: float(os.environ.get("RMS_NORM_EPS", "1e-6")))
+    xsa_projection: bool = field(default_factory=lambda: _bool_env("XSA_PROJECTION", True))
+    tie_word_embeddings: bool = field(default_factory=lambda: _bool_env("TIE_WORD_EMBEDDINGS", _bool_env("TIE_EMBEDDINGS", True)))
+
+    min_lr: float = field(default_factory=lambda: float(os.environ.get("MIN_LR", "0.0")))
+    lr: float = field(default_factory=lambda: float(os.environ.get("LR", "0.004")))
+    beta1: float = field(default_factory=lambda: float(os.environ.get("BETA1", "0.9")))
+    beta2: float = field(default_factory=lambda: float(os.environ.get("BETA2", "0.95")))
+    adam_eps: float = field(default_factory=lambda: float(os.environ.get("ADAM_EPS", "1e-8")))
+    weight_decay: float = field(default_factory=lambda: float(os.environ.get("WEIGHT_DECAY", "0.005")))
+
+    compile_model: bool = field(default_factory=lambda: _bool_env("COMPILE_MODEL", True))
+    autocast: bool = field(default_factory=lambda: _bool_env("AUTOCAST", True))
+    bf16: bool = field(default_factory=lambda: _bool_env("BF16", True))
+    device: str = field(default_factory=lambda: os.environ.get("DEVICE", "auto"))
+
+    output_dir: str = field(default_factory=lambda: _path_env("OUTPUT_DIR", "outputs"))
+    checkpoint_name: str = field(default_factory=lambda: os.environ.get("CHECKPOINT_NAME", "final_model"))
+    logfile: str = field(init=False)
+    model_path: str = field(init=False)
+    is_main_process: bool = True
+    train_files: str = field(init=False)
+    val_files: str = field(init=False)
+
+    def __post_init__(self) -> None:
+        self.rank = int(os.environ.get("RANK", "0"))
+        if self.rank < 0:
+            raise ValueError("RANK must be non-negative")
+        self.is_main_process = self.rank == 0
+        self.head_dim = int(os.environ.get("HEAD_DIM", str(self.hidden_size // self.num_attention_heads)))
+        self.train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", str(self.block_size)))
+        self.eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", os.environ.get("TRAIN_SEQ_LEN", str(self.train_seq_len))))
+        token_alignment = self.train_seq_len * self.grad_accum_steps
+        if self.train_batch_tokens % token_alignment != 0:
+            raise ValueError(
+                "TRAIN_BATCH_TOKENS must be divisible by TRAIN_SEQ_LEN * GRAD_ACCUM_STEPS"
+            )
+        requested_train_tokens = int(os.environ.get("TRAIN_TOKENS", "0"))
+        self.requested_train_tokens = requested_train_tokens or self.iterations * self.train_batch_tokens
+        if self.requested_train_tokens <= 0:
+            raise ValueError("TRAIN_TOKENS must be positive")
+        self.train_tokens = self.requested_train_tokens - (self.requested_train_tokens % token_alignment)
+        if self.train_tokens <= 0:
+            raise ValueError(
+                "TRAIN_TOKENS must provide at least TRAIN_SEQ_LEN * GRAD_ACCUM_STEPS tokens"
+            )
+        self.iterations = math.ceil(self.train_tokens / self.train_batch_tokens)
+        tokenized = Path(self.tokenized_dir)
+        self.train_files = os.environ.get("TRAIN_FILES", str(tokenized / "*" / "shard_*.bin"))
+        self.val_files = os.environ.get("VAL_FILES", os.environ.get("TRAIN_FILES", self.train_files))
+        explicit_legacy_mix = bool(os.environ.get("MIX_WEIGHTS_PATH"))
+        if not self.curriculum_path and not explicit_legacy_mix:
+            tokenized_curriculum = tokenized / "curriculum.json"
+            default_curriculum = Path("pretraining_curriculum.json")
+            if tokenized_curriculum.exists():
+                self.curriculum_path = str(tokenized_curriculum)
+            elif default_curriculum.exists():
+                self.curriculum_path = str(default_curriculum)
+        if not self.mix_weights_path and not self.curriculum_path:
+            mix_weights_path = tokenized / "mix_weights.json"
+            self.mix_weights_path = str(mix_weights_path) if mix_weights_path.exists() else ""
+        if not self.tokenizer_path:
+            tok_dir = Path(self.tokenizer_dir)
+            json_path = tok_dir / "tokenizer.json"
+            self.tokenizer_path = str(json_path if json_path.exists() else tok_dir)
+        out = Path(self.output_dir)
+        self.logfile = os.environ.get("LOGFILE", str(out / "logs" / f"{self.run_id}.txt"))
+        self.model_path = os.environ.get("MODEL_PATH", str(out / self.checkpoint_name))
+
+    def to_model_config(self) -> GPTConfig:
+        return GPTConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            num_key_value_heads=self.num_key_value_heads,
+            head_dim=self.head_dim,
+            intermediate_size=self.intermediate_size,
+            block_size=self.block_size,
+            rope_theta=self.rope_theta,
+            rms_norm_eps=self.rms_norm_eps,
+            xsa_projection=self.xsa_projection,
+            tie_word_embeddings=self.tie_word_embeddings,
+            labels_are_shifted=True,
+        )

configuration_gpt.py

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+
+from .config import (
+    DEFAULT_BLOCK_SIZE,
+    DEFAULT_HEAD_DIM,
+    DEFAULT_HIDDEN_SIZE,
+    DEFAULT_INTERMEDIATE_SIZE,
+    DEFAULT_NUM_ATTENTION_HEADS,
+    DEFAULT_NUM_HIDDEN_LAYERS,
+    DEFAULT_NUM_KEY_VALUE_HEADS,
+    DEFAULT_ROPE_THETA,
+    DEFAULT_VOCAB_SIZE,
+    GPTConfig,
+)
+
+
+__all__ = [
+    "DEFAULT_BLOCK_SIZE",
+    "DEFAULT_HEAD_DIM",
+    "DEFAULT_HIDDEN_SIZE",
+    "DEFAULT_INTERMEDIATE_SIZE",
+    "DEFAULT_NUM_ATTENTION_HEADS",
+    "DEFAULT_NUM_HIDDEN_LAYERS",
+    "DEFAULT_NUM_KEY_VALUE_HEADS",
+    "DEFAULT_ROPE_THETA",
+    "DEFAULT_VOCAB_SIZE",
+    "GPTConfig",
+]

generation_config.json

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+{
+  "_from_model_config": true,
+  "transformers_version": "4.57.6"
+}

merges.txt

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+#version: 0.2
+Ġ t
+Ġ a
+i n
+h e
+r e
+o n
+Ġt he
+a t
+e r
+Ġ s
+Ġ o
+Ġ c
+e n
+e s
+i s
+a l
+i t
+o r
+n d
+Ġ w
+Ġ p
+in g
+Ġ Ġ
+Ġ f
+a n
+i on
+Ġ b
+Ġo f
+e d
+o u
+Ġ m
+Ġ in
+a r
+i c
+Ġ d
+Ġa nd
+l e
+Ġt o
+r o
+c t
+0 0
+Ġ e
+en t
+Ġt h
+s t
+o m
+Ġ re
+v e
+Ġ h
+a s
+Ġ n
+e t
+Ġ is
+Ġ l
+i l
+ĠĠ ĠĠ
+at ion
+i m
+l y
+r a
+Ġ T
+o l
+c e
+Ġ g
+u t
+o w
+i g
+â Ģ
+s e
+o t
+Ġ u
+i d
+Ġf or
+u l
+Ġb e
+Ġ y
+Ġ 1
+c h
+Ġ A
+Ġ (
+Ġ I
+u r
+Ġ S
+Ġs t
+v er
+Ġ on
+Ġth at
+a m
+Ġc on
+Ġ C
+i f
+00 0
+it h
+Ġy ou
+e l
+u m
+Ġp ro
+q u
+i r
+Ġ 2
+Ġa n
+a y
+Ġa s
+Ġw ith
+o d
+at e
+Ġ it
+Ġa re
+g e
+Ġe x
+Ġ v
+Ġa l
+Ġ P
+Ġo r
+a b
+t er
+he r
+Ġ M
+e ct
+a d
+re s
+Â ł
+Ġ =
+Ġw h
+ĠT he
+er s
+is t
+it y
+Ġc om
+m ent
+es s
+i v
+i ve
+Ġd e
+ĠĠĠĠ ĠĠĠĠ
+â Ķ
+i es
+Ġw e
+p l
+t h
+t e
+ig h
+o s
+es t
+Ġc an
+o re
+Ġ B
+Ġs u
+u s
+Ġ F
+Ġ r
+il l
+i al
+k e
+Ġ E
+u n
+o p
+en d
+- -
+Ġb y
+a c
+a in
+Ġ D
+e m
+an t
+Ġ he
+n t
+u re
+a nd
+Ġ W
+Ġ R
+âĶ Ģ
+p p
+r i
+ro m
+ion s
+ic al
+âĶĢ âĶĢ
+Ġ H
+igh t
+Ġa t
+o c
+Ġc h
+l d
+Ġs e
+Ġth is
+Ġn e
+Ġh a
+Ġ le
+in e
+âĢ Ļ
+u d
+or t
+Ġ \
+ar t
+Ġ L
+Ġf rom
+Ġ 0
+p er
+p t
+Ġs h
+Ġu s
+o g
+Ġ âĢ
+p e
+Ġ N
+p le
+i z
+. .
+al l
+Ġ 3
+a ct
+# #
+a g
+Ġn ot
+ation s
+Ġ en
+r ou
+ul t
+Ġha ve
+d u
+Ġ im
+e re
+Ġa b
+Ġ G
+ct ion
+Ġ -
+a ge
+Ġyou r
+f f
+Ġ k
+Ġ $
+a re
+ou r
+or m
+om e
+ou t
+o st
+Ġw or
+Ġw he
+ic h
+Ġ O
+Ġre s
+u st
+ou ld
+x t
+ar d
+at ed
+Ġp l
+an ce
+l ow
+T he
+u e
+ab le
+g h
+an s
+id e
+t her
+Ġa d
+b er
+b le
+ĠT h
+Ġm e
+ĠĠ Ġ
+a st
+in d
+ou s
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model.py

@@ -0,0 +1,288 @@
+import math
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+from torch.nn import functional as F
+from transformers import PreTrainedModel
+from transformers.cache_utils import DynamicCache
+from transformers.generation.utils import GenerationMixin
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from .config import GPTConfig
+
+
+CONTROL_TENSOR_NAME_PATTERNS = (
+    "scale",
+    "gate",
+    "gain",
+    "norm",
+    "ln_",
+    "rms",
+)
+
+
+class CastedLinear(nn.Linear):
+    """Store linear params in FP32, cast to activation dtype for matmul."""
+
+    def forward(self, x: Tensor) -> Tensor:
+        weight = self.weight.to(dtype=x.dtype)
+        bias = self.bias.to(dtype=x.dtype) if self.bias is not None else None
+        return F.linear(x, weight, bias)
+
+
+def restore_fp32_params(model: nn.Module) -> None:
+    """Keep linear weights and control params in FP32 after dtype conversion."""
+    for module in model.modules():
+        if isinstance(module, CastedLinear):
+            module.float()
+    for name, param in model.named_parameters():
+        if (
+            param.ndim < 2
+            or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)
+        ) and param.dtype != torch.float32:
+            param.data = param.data.float()
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        rms = torch.rsqrt(x.float().pow(2).mean(-1, keepdim=True) + self.eps)
+        return (x.float() * rms).to(dtype=x.dtype) * self.weight.to(dtype=x.dtype)
+
+
+def build_rope_inv_freq(head_dim, theta=2500.0):
+    return 1.0 / (theta ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim))
+
+
+def precompute_rope_cos_sin(head_dim, seq_len, theta=2500.0):
+    freqs = build_rope_inv_freq(head_dim, theta)
+    t = torch.arange(seq_len, dtype=torch.float32)
+    freqs = torch.outer(t, freqs)
+    return freqs.cos(), freqs.sin()
+
+
+def _apply_rope(x, cos, sin):
+    x_float = x.float()
+    x_pair = x_float.reshape(*x_float.shape[:-1], -1, 2)
+    even = x_pair[..., 0]
+    odd = x_pair[..., 1]
+    cos = cos.unsqueeze(0).unsqueeze(0)
+    sin = sin.unsqueeze(0).unsqueeze(0)
+    x_rot = torch.stack((even * cos - odd * sin, even * sin + odd * cos), dim=-1)
+    return x_rot.flatten(-2).type_as(x)
+
+
+def apply_rotary_emb(q, k, freqs_cis):
+    cos, sin = freqs_cis
+    return _apply_rope(q, cos, sin), _apply_rope(k, cos, sin)
+
+class GPTAttention(nn.Module):
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.n_head = config.num_attention_heads
+        self.n_kv_heads = config.num_key_value_heads
+        self.head_dim = config.head_dim
+        self.n_rep = self.n_head // self.n_kv_heads
+        self.xsa_projection = config.xsa_projection
+
+        self.q_proj = CastedLinear(config.hidden_size, self.n_head * self.head_dim, bias=False)
+        self.k_proj = CastedLinear(config.hidden_size, self.n_kv_heads * self.head_dim, bias=False)
+        self.v_proj = CastedLinear(config.hidden_size, self.n_kv_heads * self.head_dim, bias=False)
+        self.o_proj = CastedLinear(self.n_head * self.head_dim, config.hidden_size, bias=False)
+
+    def _xsa_efficient(self, y: Tensor, v_current: Tensor) -> Tensor:
+        B, H, T, D = y.shape
+        Hkv = v_current.size(1)
+        group = H // Hkv
+
+        y_g = y.reshape(B, Hkv, group, T, D)
+        v_n = F.normalize(v_current, dim=-1).unsqueeze(2)
+
+        proj = (y_g * v_n).sum(dim=-1, keepdim=True) * v_n
+        return (y_g - proj).reshape(B, H, T, D)
+
+    def forward(self, x, freqs_cis, past_key_value=None, use_cache=False, attention_mask=None):
+        B, T, _ = x.size()
+
+        q = self.q_proj(x).view(B, T, self.n_head, self.head_dim).transpose(1, 2)
+        k_current = self.k_proj(x).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2)
+        v_current = self.v_proj(x).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2)
+
+        q, k_current = apply_rotary_emb(q, k_current, freqs_cis)
+
+        if past_key_value is not None:
+            k, v = past_key_value.update(k_current, v_current, self.layer_idx)
+        else:
+            k, v = k_current, v_current
+
+        S = k.size(2)
+
+        is_causal = past_key_value is None or past_key_value.get_seq_length(self.layer_idx) == T
+
+        attn_mask = None
+        if attention_mask is not None:
+            key_pad = attention_mask.to(torch.bool)[:, None, None, :]
+
+            if is_causal and T > 1:
+                causal = torch.ones(T, S, dtype=torch.bool, device=x.device).tril(diagonal=S - T)
+                attn_mask = key_pad & causal[None, None, :, :]
+            else:
+                attn_mask = key_pad.expand(B, 1, T, S)
+
+            is_causal = False
+
+        y = F.scaled_dot_product_attention(
+            q,
+            k,
+            v,
+            attn_mask=attn_mask,
+            is_causal=is_causal,
+            enable_gqa=(self.n_kv_heads != self.n_head),
+        )
+
+        if self.xsa_projection:
+            y = self._xsa_efficient(y, v_current)
+
+        y = y.transpose(1, 2).contiguous().view(B, T, self.n_head * self.head_dim)
+        return self.o_proj(y)
+
+
+class GPTMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.w_gate = CastedLinear(config.hidden_size, config.intermediate_size, bias=False)
+        self.w_up = CastedLinear(config.hidden_size, config.intermediate_size, bias=False)
+        self.w_down = CastedLinear(config.intermediate_size, config.hidden_size, bias=False)
+
+    def forward(self, x):
+        return self.w_down(F.silu(self.w_gate(x)) * self.w_up(x))
+
+
+class GPTBlock(nn.Module):
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.ln_1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.attn = GPTAttention(config, layer_idx)
+        self.ln_2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.mlp = GPTMLP(config)
+
+    def forward(self, x, freqs_cis, past_key_value=None, use_cache=False, attention_mask=None):
+        x = x + self.attn(self.ln_1(x), freqs_cis, past_key_value, use_cache, attention_mask=attention_mask)
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class GPTPreTrainedModel(PreTrainedModel):
+    config_class = GPTConfig
+    base_model_prefix = "transformer"
+    supports_gradient_checkpointing = False
+
+    def _init_weights(self, module):
+        std = self.config.hidden_size ** -0.5
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+
+
+class GPTForCausalLM(GPTPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = {"lm_head.weight": "transformer.wte.weight"}
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        self.transformer = nn.ModuleDict(dict(
+            wte=nn.Embedding(config.vocab_size, config.hidden_size),
+            h=nn.ModuleList([GPTBlock(config, i) for i in range(config.num_hidden_layers)]),
+            ln_f=RMSNorm(config.hidden_size, eps=config.rms_norm_eps),
+        ))
+        self.lm_head = CastedLinear(config.hidden_size, config.vocab_size, bias=False)
+        if config.tie_word_embeddings:
+            self.lm_head.weight = self.transformer["wte"].weight
+        self._freqs_cis_cache = None
+        self.post_init()
+        restore_fp32_params(self)
+
+    def _apply(self, fn):
+        module = super()._apply(fn)
+        restore_fp32_params(self)
+        return module
+
+    def get_input_embeddings(self):
+        return self.transformer["wte"]
+
+    def set_input_embeddings(self, value):
+        self.transformer["wte"] = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **kwargs):
+        if past_key_values is not None and past_key_values.get_seq_length() > 0:
+            input_ids = input_ids[:, -1:]
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "past_key_values": past_key_values,
+            "use_cache": True,
+        }
+
+    def _get_freqs_cis(self, seq_len, device):
+        cache = self._freqs_cis_cache
+        if cache is None or cache[0].device != device or cache[0].size(0) < seq_len:
+            cache = tuple(
+                tensor.to(device)
+                for tensor in precompute_rope_cos_sin(self.config.head_dim, seq_len, self.config.rope_theta)
+            )
+            if torch.is_inference_mode_enabled():
+                return cache[0][:seq_len], cache[1][:seq_len]
+            self._freqs_cis_cache = cache
+        return cache[0][:seq_len], cache[1][:seq_len]
+
+    def forward(
+        self,
+        input_ids,
+        attention_mask=None,
+        labels=None,
+        past_key_values: Optional[DynamicCache] = None,
+        use_cache=False,
+        **kwargs,
+    ):
+        B, T = input_ids.size()
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        past_len = past_key_values.get_seq_length() if past_key_values is not None else 0
+        x = self.transformer["wte"](input_ids)
+        freqs_cis = self._get_freqs_cis(past_len + T, input_ids.device)[past_len:]
+
+        for block in self.transformer["h"]:
+            x = block(x, freqs_cis, past_key_values if use_cache else None, use_cache, attention_mask=attention_mask)
+
+        x = self.transformer["ln_f"](x)
+        logits = self.lm_head(x)
+
+        loss = None
+        if labels is not None:
+            if getattr(self.config, "labels_are_shifted", False):
+                loss = F.cross_entropy(logits.float().reshape(-1, logits.size(-1)), labels.reshape(-1))
+            else:
+                shift_logits = logits[..., :-1, :].contiguous()
+                shift_labels = labels[..., 1:].contiguous()
+                loss = F.cross_entropy(shift_logits.float().view(-1, shift_logits.size(-1)), shift_labels.reshape(-1))
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=past_key_values if use_cache else None,
+        )

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d2f5b69edddb13b236e7954fa6252249955a5b06e764b7cc64ba3c1f7b9e5ed9
+size 13657760

special_tokens_map.json

@@ -0,0 +1,9 @@
+{
+  "additional_special_tokens": [
+    "<|endoftext|>"
+  ],
+  "bos_token": "<|bos|>",
+  "eos_token": "<|eos|>",
+  "pad_token": "<|pad|>",
+  "unk_token": "<|unk|>"
+}

tokenizer_config.json

@@ -0,0 +1,11 @@
+{
+  "additional_special_tokens": [
+    "<|endoftext|>"
+  ],
+  "bos_token": "<|bos|>",
+  "eos_token": "<|eos|>",
+  "model_max_length": 512,
+  "pad_token": "<|pad|>",
+  "tokenizer_class": "GPT2TokenizerFast",
+  "unk_token": "<|unk|>"
+}

vocab.json

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