Upload PicoDecoderHF

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
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+## How to Get Started with the Model
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+## Training Details
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+#### Training Hyperparameters
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+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
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config.json

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+{
+  "activation_hidden_dim": 3072,
+  "architectures": [
+    "PicoDecoderHF"
+  ],
+  "attention_n_heads": 12,
+  "attention_n_kv_heads": 4,
+  "auto_map": {
+    "AutoConfig": "pico_decoder.PicoDecoderHFConfig",
+    "AutoModelForCausalLM": "pico_decoder.PicoDecoderHF"
+  },
+  "batch_size": 64,
+  "d_model": 768,
+  "max_seq_len": 128,
+  "model_type": "pico_decoder",
+  "n_layers": 12,
+  "norm_eps": 1e-06,
+  "position_emb_theta": 10000.0,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.0",
+  "vocab_size": 50304
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7bee4b846dece95fa18cf458179c02088b8427b40d4fdcc65feb74f4376c2441
+size 724393544

pico_decoder.py

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+"""
+Pico Decoder: A Lightweight Causal Transformer Language Model
+
+Pico Decoder uses a simple LLAMA-style transformer architecture, written for clarity and educational purposes.
+
+Everything is written with a modular design for easy modification and experimentation.
+
+Key features:
+- RMSNorm for layer normalization
+- Rotary Positional Embeddings (RoPE)
+- Multi-head attention with KV-cache support
+- SwiGLU activation function
+- Residual connections throughout
+
+- KV-cache for faster autoregressive generation
+
+References:
+    - RoPE: https://arxiv.org/abs/2104.09864
+    - SwiGLU: https://arxiv.org/abs/2002.05202
+    - LLAMA: https://arxiv.org/abs/2302.13971
+
+Adapted from:
+    - OLMO: https://github.com/allenai/OLMo
+    - LLAMA: https://github.com/meta/llama
+"""
+
+from dataclasses import asdict
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.attention import SDPBackend, sdpa_kernel
+from transformers import PretrainedConfig, PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutput, CausalLMOutputWithPast
+
+try:
+    if TYPE_CHECKING:
+        # We need to do this to avoid importing these when creating the HF-compatible models
+        from src.config import ModelConfig
+except ImportError:
+    pass
+
+########################################################
+#
+# Layer Normalization
+#
+########################################################
+
+
+class RMSNorm(torch.nn.Module):
+    """Root Mean Square Layer Normalization.
+
+    A variant of Layer Normalization that uses RMS statistics instead of mean/variance,
+    resulting in improved stability and performance.
+
+    Args:
+        config (Union[ModelConfig, PicoHFConfig]): Configuration object containing normalization parameters
+            - config.norm_eps: Small constant for numerical stability
+            - config.d_model: Model dimension for the weight parameter
+
+    References:
+        https://arxiv.org/abs/1910.07467
+    """
+
+    def __init__(self, config: Union["ModelConfig", "PicoDecoderHFConfig"]):
+        super().__init__()
+        self.eps = config.norm_eps
+        self.weight = nn.Parameter(torch.ones(config.d_model))
+
+    def _norm(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Normalizes the input tensor by its RMS value.
+        """
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Applies RMS normalization to the input tensor and scales it by the weight parameter.
+        """
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+########################################################
+#
+# Positional Embedding
+#
+########################################################
+
+
+class RoPE(nn.Module):
+    """Rotary Positional Embeddings (RoPE).
+
+    Implements position-dependent rotation of keys and queries in attention mechanism,
+    allowing better modeling of relative positions in sequences. Uses complex number
+    operations for efficient rotation.
+
+    Args:
+        config (Union[ModelConfig, PicoHFConfig]): Model configuration containing:
+            - config.position_emb_theta: Base for frequency computation
+            - config.d_model: Model dimension
+            - config.attention_n_heads: Number of attention heads
+            - config.max_seq_len: Maximum sequence length
+
+    References:
+        https://arxiv.org/abs/2104.09864
+    """
+
+    _freqs_cis_tensor: torch.Tensor | None = None
+
+    def __init__(self, config: Union["ModelConfig", "PicoDecoderHFConfig"]):
+        super().__init__()
+
+        self.theta = config.position_emb_theta
+        self.dim = config.d_model // config.attention_n_heads
+
+        max_seq_len = config.max_seq_len
+
+        # only gets set once, and then reused for all RoPE instances
+        if RoPE._freqs_cis_tensor is None:
+            RoPE._freqs_cis_tensor = self._setup_freqs_cis(
+                max_seq_len, self.theta, self.dim
+            )
+
+        # register _freqs_cis buffer
+        # can be easily recomputed so persistent=False
+        self.register_buffer("_freqs_cis", self._freqs_cis_tensor, persistent=False)
+
+    @classmethod
+    def _setup_freqs_cis(cls, seq_len: int, theta: float, dim: int) -> torch.Tensor:
+        """Setup Frequency Tensor for RoPE Embeddings
+
+        Initializes the complex frequency tensor that is used to compute the RoPE embeddings.
+
+        Note other implementations will use cos and sin directly, but using the complex
+        number representation is (probably?) more efficient:
+
+            e^(theta * i * t) = cos(theta * t) + i * sin(theta * t) [Euler's formula]
+        """
+        _freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+        positions = torch.arange(seq_len)
+        freqs = torch.outer(positions, _freqs)
+        return torch.polar(torch.ones_like(freqs), freqs)  # complex64
+
+    def get_freqs_cis(
+        self, input_shape: torch.Size, start_pos: int, end_pos: int
+    ) -> torch.Tensor:
+        """Reshape Frequency Tensor for RoPE Embeddings
+
+        Makes the frequency tensor broadcastable with the input tensor.
+        """
+        _freqs_cis = self._freqs_cis[start_pos:end_pos]
+        ndim = len(input_shape)
+        assert 0 <= 1 < ndim
+        assert _freqs_cis.shape == (input_shape[1], input_shape[-1])
+
+        # TODO: Check whether this is correct (might be able to remove this)
+        shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(input_shape)]
+        return _freqs_cis.view(*shape)
+
+    def forward(
+        self,
+        queries: torch.Tensor,
+        keys: torch.Tensor,
+        start_pos: int = 0,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Apply RoPE Embeddings to Queries and Keys
+
+        Applies the rotary positional embeddings to the input tensors via complex num multiplication
+
+        NOTE: The start_pos is used if we want to use the kv_cache in the attention mechanism.
+        """
+        queries_ = torch.view_as_complex(
+            queries.float().reshape(*queries.shape[:-1], -1, 2)
+        )
+        keys_ = torch.view_as_complex(keys.float().reshape(*keys.shape[:-1], -1, 2))
+
+        input_shape = (
+            queries_.shape
+        )  # same as keys: (batch_size, seq_len, n_heads, head_dim/2)
+        freqs_start_pos = start_pos
+        freqs_end_pos = freqs_start_pos + queries_.shape[1]
+
+        freqs_cis = self.get_freqs_cis(input_shape, freqs_start_pos, freqs_end_pos)
+
+        queries_rotated = torch.view_as_real(queries_ * freqs_cis).flatten(3)
+        keys_rotated = torch.view_as_real(keys_ * freqs_cis).flatten(3)
+        return queries_rotated.type_as(queries), keys_rotated.type_as(keys)
+
+
+########################################################
+#
+# Attention
+#
+########################################################
+
+
+class Attention(nn.Module):
+    """Multi-head Attention with Group Query Attention support.
+
+    Implements scaled dot-product attention and supports:
+    - Grouped Query Attention (GQA)
+    - Key-Value caching for efficient inference
+    - RoPE integration
+
+    Args:
+        config (Union[ModelConfig, PretrainedConfig]): Configuration containing:
+            - config.attention_n_heads: Number of attention heads
+            - config.attention_n_kv_heads: Number of key/value heads
+            - config.d_model: Model dimension
+            - config.batch_size: Maximum batch size
+            - config.max_seq_len: Maximum sequence length
+
+    Shape:
+        - Input: (batch_size, seq_len, d_model)
+        - Output: (batch_size, seq_len, d_model)
+    """
+
+    def __init__(
+        self,
+        config: Union["ModelConfig", "PicoDecoderHFConfig"],
+    ):
+        super().__init__()
+
+        self.n_heads = config.attention_n_heads
+        self.n_kv_heads = config.attention_n_kv_heads
+
+        self.batch_size = config.batch_size
+        self.max_seq_len = config.max_seq_len
+
+        d_model = config.d_model
+        self.head_dim = d_model // self.n_heads
+
+        self.n_rep = self.n_heads // self.n_kv_heads
+
+        self.q_proj = nn.Linear(d_model, self.n_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(d_model, self.n_kv_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(d_model, self.n_kv_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.n_heads * self.head_dim, d_model, bias=False)
+
+        self.rope = RoPE(config)
+
+    def forward(
+        self,
+        input: torch.Tensor,
+        mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[torch.Tensor, ...]] = None,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+        """Forward pass for the attention mechanism.
+
+        Computes queries, keys, and values for the attention mechanism. Applies rotary positional
+        embeddings to the queries and keys, and then computes attention scores and outputs.
+
+        For an introduction to the attention mechanism, see:
+        https://arxiv.org/abs/1706.03762
+
+        A few things to note:
+        - The past_key_values is used to implement the KV cache, which is used to speed up
+          generation by caching the KV pairs from previous forward passes. This is useful when doing
+          tasks that require generating multiple tokens conditioned on previous tokens (e.g. language
+          modeling, text generation, etc.). The way the KV cache is implemented is that each layer has
+          its own KV cache - this KV cache is implemented as a tuple.
+        """
+        bsz, seq_len, _ = input.shape
+        _queries, _keys, _values = (
+            self.q_proj(input),
+            self.k_proj(input),
+            self.v_proj(input),
+        )
+
+        # Reshaping for multi-head attention
+        queries = _queries.view(bsz, seq_len, self.n_heads, self.head_dim)
+        keys = _keys.view(bsz, seq_len, self.n_kv_heads, self.head_dim)
+        values = _values.view(bsz, seq_len, self.n_kv_heads, self.head_dim)
+
+        # The start position is used to apply the RoPE embeddings to only the new tokens
+        # when using the kv_cache in the attention mechanism.
+        # We want to start from the last position in the cache.
+        start_pos = past_key_values[0].shape[1] if past_key_values is not None else 0
+
+        # apply rotary positional embeddings
+        queries, keys = self.rope(queries, keys, start_pos)
+
+        if past_key_values is not None:
+            keys = torch.cat([past_key_values[0], keys], dim=1)
+            values = torch.cat([past_key_values[1], values], dim=1)
+
+        if use_cache:
+            cached_keys = keys
+            cached_values = values
+        else:
+            cached_keys = None
+            cached_values = None
+
+        queries = queries.transpose(1, 2)
+        keys = keys.transpose(1, 2)
+        values = values.transpose(1, 2)
+
+        apply_gqa = self.n_rep > 1
+        if apply_gqa and queries.device.type == "mps":
+            # NOTE: MPS does not support GQA in the SDPA kernel, but we can repeat the keys and values
+            # outside of the kernel to get the same effect.
+            # See: https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
+            keys = keys.repeat_interleave(self.n_rep, dim=-3)
+            values = values.repeat_interleave(self.n_rep, dim=-3)
+            apply_gqa = False
+
+        backends = [SDPBackend.CUDNN_ATTENTION, SDPBackend.MATH]
+
+        with sdpa_kernel(backends=backends):
+            attn_output = F.scaled_dot_product_attention(
+                queries.contiguous(),
+                keys.contiguous(),
+                values.contiguous(),
+                attn_mask=mask.to(queries.dtype),
+                enable_gqa=apply_gqa,
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, seq_len, -1)
+        output = self.o_proj(attn_output)
+
+        return output, (cached_keys, cached_values)
+
+
+########################################################
+#
+# SwiGLU (Combines MLP and Activation)
+#
+########################################################
+
+
+class SwiGLU(nn.Module):
+    """SwiGLU Activation Function with Linear Projections.
+
+    Implements the SwiGLU activation function combined with linear transformations,
+    serving as the feed-forward network in transformer blocks.
+
+    Args:
+        config (Union[ModelConfig, PicoDecoderHFConfig]): Configuration containing:
+            - config.d_model: Model dimension
+            - config.activation_hidden_dim: Hidden dimension (typically 4 * d_model)
+
+    References:
+        https://arxiv.org/abs/2002.05202
+    """
+
+    def __init__(self, config: Union["ModelConfig", "PicoDecoderHFConfig"]):
+        super().__init__()
+
+        model_dim = config.d_model
+        act_hidden_dim = config.activation_hidden_dim  # usually 4 * d_model
+
+        self.w_0 = nn.Linear(model_dim, act_hidden_dim, bias=False)
+        self.w_1 = nn.Linear(model_dim, act_hidden_dim, bias=False)
+        self.w_2 = nn.Linear(act_hidden_dim, model_dim, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.w_2(F.silu(self.w_0(x)) * self.w_1(x))
+
+
+########################################################
+#
+# PicoDecoderBlock
+#
+########################################################
+
+
+class PicoDecoderBlock(nn.Module):
+    """Single Transformer Block with Attention and Feed-forward layers.
+
+    Implements a standard transformer block with:
+    - Multi-head attention with normalization and residual connection
+    - SwiGLU feed-forward network with normalization and residual connection
+
+    Args:
+        config (Union[ModelConfig, PicoDecoderHFConfig]): Model configuration; either a dataclass or
+            a HuggingFace PicoDecoderHFConfig
+    """
+
+    def __init__(
+        self,
+        config: Union["ModelConfig", "PicoDecoderHFConfig"],
+    ):
+        super().__init__()
+
+        self.attention = Attention(config)
+        self.swiglu = SwiGLU(config)
+        self.attention_norm = RMSNorm(config)
+        self.swiglu_norm = RMSNorm(config)
+
+    def forward(
+        self,
+        input: torch.Tensor,
+        mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[torch.Tensor]] = None,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+        attention_output, cached_key_values = self.attention(
+            self.attention_norm(input),
+            mask=mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+        )
+        # NOTE: cached_key_values is None if use_cache is False
+
+        h = input + attention_output
+        out = h + self.swiglu(self.swiglu_norm(h))
+        return out, cached_key_values
+
+
+########################################################
+#
+# Pico Decoder (Causal Transformer Model)
+#
+########################################################
+
+
+class PicoDecoder(nn.Module):
+    """
+    Pico Decoder: combines the embedding, causal decoder blocks, and output projection into a
+    single autoregressive model.
+
+    For more information on the model, see the classes for the modules that make up the model.
+    """
+
+    def __init__(
+        self,
+        model_config: Union["ModelConfig", "PicoDecoderHFConfig"],
+    ):
+        super().__init__()
+        self.config = model_config
+
+        self.embedding_proj = nn.Embedding(self.config.vocab_size, self.config.d_model)
+        self.layers = nn.ModuleList(
+            [PicoDecoderBlock(self.config) for _ in range(self.config.n_layers)]
+        )
+        self.output_norm = RMSNorm(self.config)
+        self.de_embedding_proj = nn.Linear(
+            self.config.d_model, self.config.vocab_size, bias=False
+        )
+
+    def convert_to_hf_model(self) -> "PicoDecoderHF":
+        """Convert the Lightning model to a HuggingFace model."""
+        # Create HF config without fabric-specific settings
+        hf_config = PicoDecoderHFConfig.from_dataclass(self.config)
+
+        # Create new HF model
+        hf_model = PicoDecoderHF(hf_config)
+
+        # Copy state dict, excluding fabric-specific keys
+        hf_model.load_state_dict(self.state_dict(prefix="pico_decoder."))
+
+        return hf_model
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[Tuple[Tuple[torch.Tensor, torch.Tensor]]]]:
+        """
+        This is the forward pass for the entire Pico model. It boils down to:
+        - Embedding the input ids
+        - Creating a causal mask
+        - Processing through the pico layers
+        - Projecting the output to logits
+
+        NOTE: One feature that might be confusing is the KV cache. The KV cache is used to speed up
+        generation by caching the KV pairs from previous forward passes. This is useful when doing
+        tasks that require generating multiple tokens conditioned on previous tokens (e.g. language
+        modeling, text generation, etc.). The way the KV cache is implemented is that each layer has
+        its own KV cache which is stored as a tuple. The whole model then stores a tuple of these
+        KV caches (so a tuple of tuples).
+        """
+
+        seq_len = input_ids.shape[-1]
+        h = self.embedding_proj(input_ids)
+
+        # Calculate start position from past cached KV pairs. Remember that each layer has its
+        # own KV Cache. So when we index past_key_values, we need to index into the KV pairs for the
+        # correct layer and then for either the keys or values.
+        start_pos = 0 if past_key_values is None else past_key_values[0][0].shape[1]
+
+        # Create causal mask for current sequence
+        mask = None
+        if seq_len > 1:
+            mask = torch.full((seq_len, seq_len), float("-inf"))
+            mask = torch.triu(mask, diagonal=1)
+
+            # If using KV cache, extend mask to cover cached sequence length
+            if past_key_values is not None:
+                # Add zeros for cached tokens (we can attend to all of them)
+                mask = torch.hstack([torch.zeros((seq_len, start_pos)), mask])
+
+            mask = mask.to(h.device)
+
+        # NOTE: If we are using the cache, we need to store the cached KV pairs for each layer
+        #       in a tuple. Each layer will have its own cached KV pair which we aggregate in a tuple.
+        cached_key_values = () if use_cache else None
+
+        # Process through transformer blocks
+        for idx, layer in enumerate(self.layers):
+            layer_past_key_values = (
+                past_key_values[idx] if past_key_values is not None else None
+            )
+
+            h, layer_cached_key_values = layer(
+                h, mask=mask, past_key_values=layer_past_key_values, use_cache=use_cache
+            )
+
+            if use_cache:
+                cached_key_values += (layer_cached_key_values,)
+
+        # Final norm and projection
+        h = self.output_norm(h)
+        logits = self.de_embedding_proj(h).float()
+
+        return logits, cached_key_values
+
+
+########################################################
+#
+# HuggingFace Wrapper for the Pico Decoder model.
+#
+########################################################
+
+
+class PicoDecoderHFConfig(PretrainedConfig):
+    """Config class for the Pico Decoder HuggingFace wrapper."""
+
+    model_type = "pico_decoder"
+
+    @classmethod
+    def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> "PicoDecoderHFConfig":
+        """
+        Initialize config from a dictionary. Note that no kwargs are passed to the constructor --
+        this is because with some kwargs special handling is required and can make this class
+        brittle.
+        """
+        pico_config = cls(**config_dict)
+
+        return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
+        unused_kwargs = {
+            key: value for key, value in kwargs.items() if not hasattr(pico_config, key)
+        }
+
+        if return_unused_kwargs:
+            return pico_config, unused_kwargs
+        return pico_config
+
+    @classmethod
+    def from_dataclass(cls, model_config: "ModelConfig"):
+        """Initialise from our custom config dataclass."""
+        return cls.from_dict(asdict(model_config))
+
+
+class PicoDecoderHF(PreTrainedModel):
+    """
+    HuggingFace wrapper for the Pico model.
+
+    Many evaluation frameworks require a model be setup as a HuggingFace model, so we provide a simple
+    wrapper that does just that. When we save checkpoints of the Pico model, we save both the normal
+    Pico model as well as the model wrapped in this HuggingFace class.
+
+    This also lets you do cool things like:
+
+    `model = AutoModelForCausalLM.from_pretrained("path/to/checkpoint")`
+    """
+
+    config_class = PicoDecoderHFConfig
+    _no_split_modules = ["PicoBlock", "Attention", "SwiGLU", "RMSNorm"]
+
+    def __init__(self, config: PicoDecoderHFConfig):
+        super().__init__(config)
+        self.pico_decoder = PicoDecoder(config)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Union[CausalLMOutput, CausalLMOutputWithPast]:
+        """HuggingFace forward pass wrapper.
+
+        Forwards pass for the HuggingFace version of the Pico Model. Basic wrapper around the
+        Pico model's forward pass, and returns the output as a HuggingFace CausalLMOutput.
+        """
+        logits, past_key_values = self.pico_decoder(
+            input_ids, past_key_values, use_cache
+        )
+        if use_cache:
+            return CausalLMOutputWithPast(
+                logits=logits,
+                past_key_values=past_key_values,
+            )
+        else:
+            return CausalLMOutput(
+                logits=logits,
+            )
+
+
+# Register for auto classes
+PicoDecoderHFConfig.register_for_auto_class()
+PicoDecoderHF.register_for_auto_class("AutoModel")
+PicoDecoderHF.register_for_auto_class("AutoModelForCausalLM")