Upload BulkRNABert

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.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+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).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

bulkrnabert.py

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+import logging
+from dataclasses import dataclass, field
+from typing import Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F  # noqa: N812
+from transformers import PretrainedConfig, PreTrainedModel
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        num_heads: int,
+        key_size: int,
+        add_bias_kv: bool = False,
+        value_size: Optional[int] = None,
+        model_size: Optional[int] = None,
+        name: Optional[str] = None,
+    ):
+        super().__init__()
+        if not model_size:
+            model_size = key_size
+        if not value_size:
+            value_size = key_size
+        self.model_size = model_size
+        self.key_size = key_size
+        self.value_size = value_size
+        self.add_bias_kv = add_bias_kv
+        self.name = name
+        self.num_heads = num_heads
+
+        self.w_k = nn.Linear(self.model_size, self.num_heads * self.key_size)
+        self.w_q = nn.Linear(self.model_size, self.num_heads * self.key_size)
+        self.w_v = nn.Linear(self.model_size, self.num_heads * self.value_size)
+        self.output = nn.Linear(self.num_heads * self.value_size, self.model_size)
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        attention_weight_bias: Optional[torch.Tensor] = None,
+    ) -> dict[str, torch.Tensor]:
+        """
+        Returns:
+            dictionary containing attention weights
+            and outputs.
+        """
+        key_heads = self.w_k(key).reshape(
+            (*key.shape[:-1], self.num_heads, self.key_size)
+        )
+        query_heads = self.w_q(query).reshape(
+            (*query.shape[:-1], self.num_heads, self.key_size)
+        )
+        value_heads = self.w_v(value).reshape(
+            (*value.shape[:-1], self.num_heads, self.value_size)
+        )
+        attention_weights = torch.einsum(
+            "...thd, ...Thd -> ...htT", query_heads, key_heads
+        )
+        sqrt_key_size = np.sqrt(self.key_size)
+        attention_weights = attention_weights / sqrt_key_size
+        if attention_mask is not None:
+            attention_weights = torch.where(attention_mask, attention_weights, -1e30)
+        if attention_weight_bias:
+            attention_weights = F.softmax(
+                attention_weights + attention_weight_bias, dim=-1
+            )
+        else:
+            attention_weights = F.softmax(attention_weights, dim=-1)
+        value_out = torch.einsum(
+            "...htT, ...Thd->...thd", attention_weights, value_heads
+        )
+        value_out = value_out.reshape((*value_out.shape[:-2], -1))
+        embeddings = self.output(value_out)
+
+        return {"attention_weights": attention_weights, "embeddings": embeddings}
+
+
+class SelfAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        num_heads: int,
+        embed_dim: int,
+        ffn_embed_dim: int,
+        key_size: Optional[int] = None,
+        add_bias_kv: bool = False,
+        add_bias_fnn: bool = True,
+        ffn_activation_name: str = "gelu-no-approx",
+        use_glu_in_ffn: bool = False,
+        layer_norm_eps: float = 1e-5,  # this is the default haiku value
+        pre_layer_norm: bool = True,
+        name: Optional[str] = None,
+    ):
+        super().__init__()
+        if key_size is None:
+            if embed_dim % num_heads != 0:
+                raise ValueError(
+                    f"The embedding dimension should be divisible by the number of "
+                    f"heads, however provided embedding dimension is {embed_dim} and "
+                    f"the number of heads is {num_heads}."
+                )
+            else:
+                key_size = embed_dim // num_heads
+
+        # Get ffn activation function
+        self._pre_layer_norm = pre_layer_norm
+        self._use_glu_in_fnn = use_glu_in_ffn
+        # Define layers
+        if use_glu_in_ffn:
+            # user should multiply ffn_embed_dim by 2/3 when using GLU
+            # to keep total number of parameters equal
+            # see https://arxiv.org/pdf/2002.05202.pdf. for more details
+            # we multiply by 2 here as the output will be split in 2 for GLU
+            self.fc1 = nn.Linear(embed_dim, int(2 * ffn_embed_dim), bias=add_bias_fnn)
+        else:
+            self.fc1 = nn.Linear(embed_dim, ffn_embed_dim, bias=add_bias_fnn)
+
+        self.fc2 = nn.Linear(ffn_embed_dim, embed_dim, bias=add_bias_fnn)
+
+        self.layer_norm_self_attention = nn.LayerNorm(
+            embed_dim,
+        )
+        self.layer_norm_mlp = nn.LayerNorm(embed_dim)
+        if ffn_activation_name == "swish":
+            self._ffn_activation_fn = nn.SiLU()
+        elif ffn_activation_name == "gelu-no-approx":
+            self._ffn_activation_fn = lambda x: F.gelu(x, approximate="none")
+        else:
+            self._ffn_activation_fn = getattr(torch.nn, ffn_activation_name)
+
+        self.mha = MultiHeadAttention(
+            num_heads=num_heads,
+            key_size=key_size,
+            add_bias_kv=add_bias_kv,
+            model_size=embed_dim,
+            name="self_attention",
+        )
+
+    def mlp(self, embed: torch.Tensor) -> torch.Tensor:
+
+        if self._pre_layer_norm:
+            x = self.layer_norm_mlp(embed)
+        else:
+            x = embed
+
+        if self._use_glu_in_fnn:
+            x = self.fc1(x)
+            x1, x2 = torch.split(x, split_size_or_sections=x.shape[-1] // 2, dim=-1)
+            x = self._ffn_activation_fn(x1) * x2
+        else:
+            x = self._ffn_activation_fn(self.fc1(x))
+        x = self.fc2(x)
+
+        if not self._pre_layer_norm:
+            x = self.layer_norm_mlp(x + embed)
+        return x
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        attention_weight_bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        res = x
+        if self._pre_layer_norm:
+            x = self.layer_norm_self_attention(x)
+
+        output = self.mha(
+            x,
+            x,
+            x,
+            attention_mask=attention_mask,
+            attention_weight_bias=attention_weight_bias,
+        )
+
+        if not self._pre_layer_norm:
+            output["embeddings"] = self.layer_norm_self_attention(
+                output["embeddings"] + res
+            )
+
+            x = output["embeddings"]
+        else:
+            x = output["embeddings"]
+            x = res + x
+
+        # MLP
+        if not self._pre_layer_norm:
+            x = self.mlp(x)
+        else:
+            x = x + self.mlp(x)
+
+        output["embeddings"] = x
+        return output
+
+
+@dataclass
+class BulkRNABertConfig(PretrainedConfig):
+    model_type = "BulkRNABert"
+    n_genes: int = 19_062
+    n_expressions_bins: int = 64
+    embed_dim: int = 256
+    init_gene_embed_dim: int = 200
+    use_gene_embedding: bool = True
+    project_gene_embedding: bool = True
+    num_attention_heads: int = 8
+    key_size: Optional[int] = None
+    ffn_embed_dim: int = 512
+    num_layers: int = 4
+
+    # return
+    embeddings_layers_to_save: tuple[int, ...] = field(default_factory=tuple)
+    attention_maps_to_save: list[tuple[int, int]] = field(default_factory=list)
+
+    def __post_init__(self):
+        # Validate attention key size
+        key_size = self.key_size
+        if key_size is None:
+            embed_dim = self.embed_dim
+            num_attention_heads = self.num_attention_heads
+            if not embed_dim % num_attention_heads == 0:
+                raise ValueError(
+                    f"When no key size is provided, the embedding dimension should be "
+                    f"divisible by the number of heads, however provided embedding "
+                    f"dimension is {embed_dim} and the number of heads is "
+                    f"{num_attention_heads}."
+                )
+            self.key_size = embed_dim // num_attention_heads
+
+        # Validate gene embedding projection
+        use_gene_embedding = self.use_gene_embedding
+        if use_gene_embedding:
+            init_gene_embed_dim = self.init_gene_embed_dim
+            embed_dim = self.embed_dim
+            if init_gene_embed_dim != embed_dim:
+                project_gene_embedding = self.project_gene_embedding
+                if not project_gene_embedding:
+                    logging.warning(
+                        f"Init gene embedding dimension ({init_gene_embed_dim})"
+                        f"different than embedding dimension ({embed_dim})."
+                        f"Setting `project_gene_embedding` to True"
+                    )
+                    self.project_gene_embedding = True
+
+
+class BulkRNABert(PreTrainedModel):
+    config_class = BulkRNABertConfig
+
+    def __init__(self, config: BulkRNABertConfig):
+        super().__init__(config=config)
+
+        self.expression_embedding_layer = nn.Embedding(
+            config.n_expressions_bins, config.embed_dim
+        )
+        self.gene_embedding_layer = nn.Embedding(
+            config.n_genes,
+            config.init_gene_embed_dim,
+        )
+        self.fc_gene_embedding = nn.Linear(config.init_gene_embed_dim, config.embed_dim)
+
+        attention_maps_to_save = config.attention_maps_to_save
+        self._attention_layers_to_save = list({t[0] for t in attention_maps_to_save})
+
+        self._attention_maps_per_layer_to_save = {
+            layer: [t[1] for t in attention_maps_to_save if t[0] == layer]
+            for layer in self._attention_layers_to_save
+        }
+        max_layer = max(self._attention_layers_to_save + [0])
+        if max_layer > config.num_layers:
+            raise ValueError(
+                f"You are requiring attention maps for layer {max_layer}, "
+                f"while the model has {config.num_layers} layers only."
+            )
+        self.transformer_layers = nn.ModuleList(
+            [
+                SelfAttentionBlock(
+                    num_heads=config.num_attention_heads,
+                    embed_dim=config.embed_dim,
+                    key_size=config.key_size,
+                    ffn_embed_dim=config.ffn_embed_dim,
+                    name=f"attention_layer_{layer_idx}",
+                )
+                for layer_idx in range(config.num_layers)
+            ]
+        )
+
+        self.lm_head = nn.Linear(config.embed_dim, config.n_expressions_bins)
+
+    def forward(
+        self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None
+    ) -> dict[str, torch.Tensor]:
+        outs = {}
+        x = self.expression_embedding_layer(input_ids)
+
+        if self.config.use_gene_embedding:
+            gene_indices = torch.arange(self.config.n_genes, device=x.device)
+            gene_embedding = self.gene_embedding_layer(gene_indices)
+            if self.config.project_gene_embedding:
+                gene_embedding = self.fc_gene_embedding(gene_embedding)
+            x = x + gene_embedding
+
+        outs["embeddings"] = x
+
+        if attention_mask is None:
+            batch_size, seq_length = input_ids.shape
+            attention_mask = torch.ones(  # noqa
+                (batch_size, 1, seq_length, seq_length),
+                device=input_ids.device,
+                dtype=bool,
+            )
+
+        for layer_idx, transformer in enumerate(self.transformer_layers):
+            output = transformer(x, attention_mask=attention_mask)
+            x = output["embeddings"]
+            if (layer_idx + 1) in self.config.embeddings_layers_to_save:
+                outs[f"embeddings_{(layer_idx + 1)}"] = output["embeddings"]
+            if (layer_idx + 1) in self._attention_layers_to_save:
+                for map_number in self._attention_maps_per_layer_to_save[layer_idx + 1]:
+                    dkey = f"attention_map_layer_{layer_idx + 1}_number_{map_number}"
+                    outs[dkey] = output["attention_weights"][:, map_number + 1]
+
+        outs["logits"] = self.lm_head(x)
+        return outs

config.json

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+{
+  "architectures": [
+    "BulkRNABert"
+  ],
+  "attention_maps_to_save": [],
+  "auto_map": {
+    "AutoConfig": "bulkrnabert.BulkRNABertConfig",
+    "AutoModel": "bulkrnabert.BulkRNABert"
+  },
+  "embed_dim": 256,
+  "embeddings_layers_to_save": [],
+  "ffn_embed_dim": 512,
+  "init_gene_embed_dim": 200,
+  "key_size": 32,
+  "model_type": "BulkRNABert",
+  "n_expressions_bins": 64,
+  "n_genes": 19062,
+  "num_attention_heads": 8,
+  "num_layers": 4,
+  "project_gene_embedding": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.2",
+  "use_gene_embedding": true
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b934e179e95a9f25f22ede71c7fe92132469d5bc4340c1031c9601b102a491f5
+size 24027776