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README.md

@@ -1,8 +1,56 @@
----
-license: apache-2.0
----
-# SliceX AI ELM
-This repository contains code to run SliceX AI ELM models.
+# SliceX AI™ ELM (Efficient Language Models)
+This repository contains code to run our ELM models.
+
+Models are located in the "models" folder. ELM models in this repository comes in three sizes (elm-1.0, elm-0.75 and elm-0.25) and supports the following use-cases.
+- news_classification
+- toxicity_detection
+- news_content_generation
+- news_summarization
+
+## Download ELM repo with model files
+```bash
+sudo apt-get intall git-lfs 
+git lfs install
+git clone <library_path>
+```
+(Optional) Installing git-lfs without sudo,
+```bash
+wget https://github.com/git-lfs/git-lfs/releases/download/v3.2.0/git-lfs-linux-amd64-v3.2.0.tar.gz
+tar -xzf git-lfs-linux-amd64-v3.2.0.tar.gz
+PATH=$PATH:/<absolute-path>/git-lfs-3.2.0/
+git lfs install
+```
+ 
+
+## (Optional) Setup docker
+```bash
+docker run --gpus all -it --shm-size=8g --name elm_inference --ulimit memlock=-1 --rm -v <elm_code_path>:/elm_code  nvcr.io/nvidia/pytorch:23.09-py3
+```
 
 ## Installation
-...
+```bash
+cd <elm_code>
+pip install -r requirements.txt
+```
+
+## How to use - Run ELM on a sample task (e.g., news classification)
+```bash
+python run.py <elm-model-directory>
+E.g. python run.py models/elm-0.75_news_classification
+``` 
+Prompts for the specific tasks can be found in the corresponding checkpoint directory. See an example below in the form of `models/elm-0.75_news_classification/example_prompts.json`.
+```json
+{
+    "inputs": ["GM May Close Plant in Europe  DETROIT (Reuters) - General Motors Corp. &lt;A HREF=\"http://www.investor.reuters.com/FullQuote.aspx?ticker=GM.N target=/stocks/quickinfo/fullquote\"&gt;GM.N&lt;/A&gt; will likely  cut some jobs in Europe and may close a plant there as part of  a restructuring plan under development to try to return the  region to profitability, the U.S. automaker said on Wednesday."],
+    "template": "[INST]Below is a news article. Please classify it under one of the following classes (World, Business, Sports, Sci/Tech). Please format your response as a JSON payload.\n\n### Article: {input}\n\n### JSON Response:[/INST]"
+}
+```
+
+Running the above command returns the following response
+
+```json
+{
+    "prompt": "[INST]Below is a news article. Please classify it under one of the following classes (World, Business, Sports, Sci/Tech). Please format your response as a JSON payload.\n\n### Article: GM May Close Plant in Europe  DETROIT (Reuters) - General Motors Corp. &lt;A HREF=\"http://www.investor.reuters.com/FullQuote.aspx?ticker=GM.N target=/stocks/quickinfo/fullquote\"&gt;GM.N&lt;/A&gt; will likely  cut some jobs in Europe and may close a plant there as part of  a restructuring plan under development to try to return the  region to profitability, the U.S. automaker said on Wednesday.\n\n### JSON Response:[/INST]",
+    "response": "{'text_label': 'Business'}"
+}
+```

elm/infer_elm.py

@@ -0,0 +1,132 @@
+# Copyright (c) 2024, SliceX AI, Inc. All Rights Reserved.
+
+from elm.model import *
+from elm.utils import batchify
+from transformers import AutoTokenizer
+import json
+
+
+def load_elm_model_and_tokenizer(local_path, 
+                                 model_config_dict,
+                                 device="cuda",
+                                 load_partial=True,
+                                 get_num_layers_from_ckpt=True):
+    """Load ELM model and tokenizer from local checkpoint."""
+    model_args = ModelArgs(**model_config_dict)
+    model = load_elm_model_from_ckpt(local_path, device=device, model_args=model_args, load_partial=load_partial, get_num_layers_from_ckpt=get_num_layers_from_ckpt)
+
+    tokenizer = AutoTokenizer.from_pretrained(local_path)
+    tokenizer.padding_side = "left"
+    tokenizer.truncation_side = "left"
+    return model, tokenizer
+
+
+def generate_elm_response_given_model(prompts, model, tokenizer, 
+                          device="cuda",
+                          max_ctx_word_len=1024,
+                          max_ctx_token_len=0,
+                          max_new_tokens=500,
+                          temperature=0.8, # set to 0 for greedy decoding
+                          top_k=200,
+                          return_tok_cnt=False,
+                          return_gen_only=False,
+                          early_stop_on_eos=False):
+    """Generate responses from ELM model given an input list of prompts ([str])."""
+    if max_ctx_token_len > 0:
+        inputs = tokenizer(prompts, return_tensors="pt", padding=True, truncation=True, max_length=max_ctx_token_len).to(device)
+    else:
+        prompts = [" ".join(p.split(" ")[-max_ctx_word_len:]) for p in prompts]
+        inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(device)
+    
+    results = []
+    
+    input_tok_cnt = torch.numel(inputs.input_ids)
+
+    model.eval()
+
+    out_tok_cnt = 0
+    with torch.no_grad():
+        temperature = temperature
+        top_k = top_k
+
+        outputs = model.generate(inputs.input_ids, max_new_tokens, temperature=temperature, top_k=top_k,
+                                 return_gen_only=return_gen_only)
+
+        if return_tok_cnt:
+            out_tok_cnt += torch.numel(outputs)
+
+        if early_stop_on_eos:
+            mod_outputs = []
+            for i in range(len(outputs)):
+                curr_out = outputs[i]
+
+                eos_loc_id = -1
+                for j in range(len(outputs[i])):
+                    tok_id = outputs[i][j]
+                    if tok_id == tokenizer.eos_token_id:
+                        eos_loc_id = j
+                        break
+                if eos_loc_id >= 0:
+                    curr_out = outputs[i][:eos_loc_id]
+                mod_outputs.append(curr_out)
+            outputs = mod_outputs
+        detokenized_output = tokenizer.batch_decode(outputs, skip_special_tokens=False)
+
+        results = detokenized_output
+
+    if return_tok_cnt:
+        return results, (input_tok_cnt, out_tok_cnt)
+
+    return results
+
+def generate_elm_responses(elm_model_path, 
+                           prompts, 
+                           device=None, 
+                           elm_model_config={},
+                           eval_batch_size=1,
+                           verbose=True):
+
+
+    if not device:
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(f"Setting device to {device}")
+
+    model_config_dict = {
+        "hidden_size": elm_model_config.get("hidden_size", 2048),
+        "max_inp_len": elm_model_config.get("max_inp_len", 2048),
+        "num_attention_heads": elm_model_config.get("num_attention_heads", 32),
+        "num_layers": elm_model_config.get("num_layers", 48),
+        "bits": elm_model_config.get("bits", 256),
+        "vocab_size": elm_model_config.get("vocab_size", 50304),
+        "dropout": elm_model_config.get("dropout", 0.1),
+        "use_rotary_embeddings": elm_model_config.get("use_rotary_embeddings", True)
+    }
+    
+    model, tokenizer = load_elm_model_and_tokenizer(local_path=elm_model_path, model_config_dict=model_config_dict, device=device, load_partial=True)
+
+    #prompts = [prompt if "[INST]" in prompt else f"[INST]{prompt}[/INST]" for prompt in prompts]
+    max_new_tokens = 128
+    if "classification" in elm_model_path or "detection" in elm_model_path:
+        max_new_tokens = 12
+    result = []
+    for prompt_batch in batchify(prompts, eval_batch_size):
+        responses, _ = generate_elm_response_given_model(prompt_batch,
+                                                            model, 
+                                                            tokenizer, 
+                                                            device=device,
+                                                            max_ctx_word_len=1024,
+                                                            max_ctx_token_len=512,
+                                                            max_new_tokens=max_new_tokens,
+                                                            return_tok_cnt=True, 
+                                                            return_gen_only=False, 
+                                                            temperature=0.0, 
+                                                            early_stop_on_eos=True)
+    
+        for prompt, response in zip(prompt_batch, responses):
+            response = response.split("[/INST]")[-1].strip()
+            result.append(response)
+            if verbose:
+                print(json.dumps({"prompt": prompt, "response": response}, indent=4))
+                print("\n***\n")
+    return result
+    

elm/model.py

@@ -0,0 +1,418 @@
+# Copyright (c) 2024, SliceX AI, Inc. All Rights Reserved.
+
+import copy
+import inspect
+import math
+import numpy as np
+import os
+
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from elm.utils import *
+from elm.positional_embeddings import *
+
+
+def get_elm_model_map(model_name):
+    """Map the model type to corresponding class."""
+    elm_model_map = { 
+        "rambutan": RambutanSlice,
+    }
+
+    return elm_model_map.get(model_name, RambutanSlice)
+
+
+@dataclass
+class ModelArgs:
+    """ELM Model Args"""
+    model_name_or_path: str = "ELM"
+    compile_model: bool = False
+    elm_model_class: Optional[str] = "rambutan"
+    hidden_size: Optional[int] = 2048
+    max_inp_len: Optional[int] = 2048 
+    attn_window_size: Optional[int] = max_inp_len 
+    num_attention_heads: Optional[int] = 32
+    layernorm_eps: float = 1e-5
+    attention_dropout: float = 0.1
+    hidden_dropout: float = 0.1
+    num_layers: Optional[int] = 16
+    bits: Optional[int] = 256
+    vocab_size: Optional[int] = 50304
+    dropout: Optional[int] = 0.1
+    use_rotary_embeddings: Optional[bool] = True   
+    tokenizer: Optional[str] = None
+
+
+class ELM(torch.nn.Module):
+    """ELM (SliceX GPT) model."""
+    def __init__(self,
+                 model_args: ModelArgs):
+        """Initialize an ELM model instance."""
+        super().__init__()
+
+        self.model_args = model_args
+
+        elm_model_class = model_args.elm_model_class
+        hidden_size = model_args.hidden_size
+        max_inp_len = model_args.max_inp_len
+        num_attention_heads = model_args.num_attention_heads
+        layernorm_eps = model_args.layernorm_eps
+        attention_dropout = model_args.attention_dropout
+        hidden_dropout = model_args.hidden_dropout
+        num_layers = model_args.num_layers
+        bits = model_args.bits
+        vocab_size = model_args.vocab_size
+        use_rotary_embeddings = model_args.use_rotary_embeddings
+
+        layer_class = get_elm_model_map(elm_model_class)
+        
+        self.slice_transformer = torch.nn.ModuleDict(dict(
+            temb = torch.nn.Embedding(vocab_size, hidden_size),
+            pemb = torch.nn.Embedding(max_inp_len, hidden_size) if not use_rotary_embeddings else None,
+            drop = torch.nn.Dropout(hidden_dropout),
+            h = torch.nn.ModuleList([ layer_class(model_args=model_args) for _ in range(num_layers) ]),
+            ln_f = torch.nn.LayerNorm(hidden_size, eps=layernorm_eps),
+        ))
+                
+        self.lm_head = torch.nn.Linear(hidden_size, vocab_size, bias=False)
+
+        print("Number of model parameters: %.2fM" % (self.get_num_params(False)/1e6,))
+
+
+    def forward(self,
+                x: torch.Tensor,
+                attention_mask: Optional[torch.Tensor] = None,
+                targets: Optional[torch.Tensor] = None):
+        device = x.device
+        batch, seqlen = x.size()
+
+        
+        tok_emb = self.slice_transformer.temb(x)
+
+        if not self.model_args.use_rotary_embeddings:
+            pos = torch.arange(0, seqlen, dtype=torch.long, device=device)
+            pos_emb = self.slice_transformer.pemb(pos)
+            x = self.slice_transformer.drop(tok_emb + pos_emb)
+        else:
+            x = self.slice_transformer.drop(tok_emb)
+
+        tlayer_id = 0
+        ignore_index_id = -100
+        loss = torch.zeros(1).to(device)
+        loss_denom = 0
+
+        for tlayer in self.slice_transformer.h:
+            x = tlayer(x, attention_mask=attention_mask)
+
+            tlayer_id += 1
+            
+        x = self.slice_transformer.ln_f(x)
+        
+        if targets is not None:
+            logits = self.lm_head(x)
+
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_targets = targets[..., 1:].contiguous()
+            curr_loss = F.cross_entropy(shift_logits.view(-1, shift_logits.size(-1)), 
+                                        shift_targets.view(-1),
+                                        ignore_index=ignore_index_id)
+            loss += curr_loss.float()
+            loss_denom += 1
+        else:
+            logits = self.lm_head(x[:, [-1], :]) 
+
+        loss = loss / loss_denom
+
+        return logits, loss
+
+
+    def get_num_params(self, non_embedding=True):
+        """
+        Return the number of parameters in the model.
+        For non-embedding count (default), the position embeddings get subtracted.
+        This assumes parameter tying between input and final layer embeddings. Oherwise
+        If there is no parameter sharing , set the flag to False to include parameters for both layers.
+        """
+        n_params = sum(p.numel() for p in self.parameters())
+        if non_embedding and not self.model_args.use_rotary_embeddings:
+            n_params -= self.slice_transformer.pemb.weight.numel()
+        return n_params
+
+
+    @torch.no_grad()
+    def generate(self, x, max_new_tokens, temperature=0.8, top_k=200, top_p=0.9,
+                 return_gen_only=False):
+        max_inp_len = self.model_args.max_inp_len
+
+        for _ in range(max_new_tokens):
+            x_ctxt = x if x.size(1) <= max_inp_len else x[:, -max_inp_len:]
+            
+            logits, _ = self(x_ctxt)
+            
+            next_id = None
+            
+            if temperature <= 0:
+                next_id = torch.argmax(logits, dim=-1) 
+            else:
+                logits = logits[:, -1, :] / temperature
+
+                if top_k is not None:
+                    v, k = torch.topk(logits, min(top_k, logits.size(-1)))
+                    logits[logits < v[:, [-1]]] = -float('Inf')
+                
+                probs = F.softmax(logits, dim=-1)
+
+                if top_p is None:
+                    next_id = torch.multinomial(probs, num_samples=1)
+                else:
+                    next_id = sample_top_p(probs, top_p)
+            x = torch.cat((x, next_id), dim=1)
+    
+        if return_gen_only:
+            return x[:,-max_new_tokens:]
+        
+        return x
+
+
+class RambutanMLP(torch.nn.Module):
+    """RambutanMLP version of MLP module used in the ELM (SliceX GPT) Transformer block."""
+    def __init__(self, dim=768, bits=32, dropout = 0.0):
+        super(RambutanMLP, self).__init__()
+        self.dim = dim
+        self.bits = bits
+      
+        self.dropout = torch.nn.Dropout(dropout)
+
+        self.A1_c_w = torch.nn.Linear(self.dim, self.bits, bias=True)
+
+        self.Hexperts = 4
+        self.Hexpertemb = torch.nn.Embedding(self.bits, self.dim)
+        
+        self.expert_aggr = torch.nn.Linear(self.Hexperts, 1)
+
+
+    def forward(self, x):
+        h_c = torch.nn.functional.softmax(self.A1_c_w(x), dim=-1)
+                
+        v, i = torch.topk(h_c, self.Hexperts)
+
+        if len(x.size()) < 3:
+            p = v.unsqueeze(-1).expand(-1,-1,self.dim)
+        else:
+            p = v.unsqueeze(-1).expand(-1,-1,-1,self.dim)
+        
+        h_emb = p * self.Hexpertemb(i)
+
+        if len(x.size()) < 3:
+            out = self.expert_aggr(h_emb.transpose(1,2)).reshape(h_emb.size(0), -1)
+        else:
+            out = self.expert_aggr(h_emb.transpose(-2,-1)).reshape(x.size())
+
+        out = x * out 
+        out = self.dropout(out)
+            
+        return out
+
+
+class RambutanSlice(torch.nn.Module):
+    """Rambutan version of ELM (SliceX GPT) Transformer block."""
+    def __init__(self,
+                 model_args: ModelArgs):
+        super().__init__()
+
+        self.model_args = model_args
+
+        self.num_attention_heads = model_args.num_attention_heads
+        self.kv_channels = model_args.hidden_size // model_args.num_attention_heads
+        self.ln1 = torch.nn.LayerNorm(model_args.hidden_size, eps=model_args.layernorm_eps)
+        self.ln2 = torch.nn.LayerNorm(model_args.hidden_size, eps=model_args.layernorm_eps)
+        self.mlp = RambutanMLP(dim=model_args.hidden_size, bits=model_args.bits) 
+        self.cattn =  RambutanCausalSelfAttention(model_args=model_args) 
+
+
+    def forward(self,
+                x: torch.Tensor,
+                attention_mask: torch.Tensor = None):
+        res = x
+        
+        x = self.ln1(x)
+        x = self.cattn(x, attention_mask=attention_mask)
+
+        x = res + x
+        res = x
+        x = self.ln2(x)
+        x = self.mlp(x)
+
+        return x + res
+
+
+class RambutanCausalSelfAttention(torch.nn.Module):
+    """Rambutan version of self-attention module used in the ELM (SliceX GPT) transformer block."""
+
+    def __init__(self,
+                 model_args: ModelArgs):
+        super().__init__()
+
+        self.model_args = model_args
+
+        n_embd = model_args.hidden_size
+        n_head = model_args.num_attention_heads
+        bias = False 
+        dropout = model_args.attention_dropout
+
+        assert n_embd % n_head == 0
+
+        self.c_attn = torch.nn.Linear(n_embd, 3 * n_embd, bias=bias)
+
+        self.c_proj = torch.nn.Linear(n_embd, n_embd, bias=bias)
+
+        self.attn_dropout = torch.nn.Dropout(dropout)
+        self.resid_dropout = torch.nn.Dropout(dropout)
+        self.n_head = n_head
+        self.n_embd = n_embd
+        self.dropout = dropout
+
+        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
+        
+        if not self.flash:
+            print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
+        self.rotary_embeddings = (
+            RotaryEmbedding(n_embd // n_head) if model_args.use_rotary_embeddings else None
+        )
+
+
+    def forward(self, x, attention_mask: torch.Tensor = None):
+        B, T, C = x.size()
+        device = x.device
+
+        q, k, v  = self.c_attn(x).split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+
+        if self.rotary_embeddings:
+            q, k = self.rotary_embeddings(q=q, k=k)
+        
+        is_causal = True
+        attn_mask = None
+
+        if attention_mask is not None:
+            att_mask_input = attention_mask 
+            att_mask_input = att_mask_input.unsqueeze(-1).expand(B, T, T)
+
+            if is_causal:
+                att_mask_causal = torch.tril(torch.ones(T, T)).view(1,T,T).expand(B,T,T).to(device) 
+                attn_mask = (att_mask_causal * att_mask_input)
+            else:
+                attn_mask = att_mask_input
+            
+            attn_mask = attn_mask.unsqueeze(1).expand(B, self.n_head, T, T)
+            attn_mask.float().to(device)
+
+       
+        if self.flash:
+            y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, dropout_p=self.dropout if self.training else 0, is_causal=True)
+        else:
+            att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+            
+            if is_causal and attn_mask is None:
+                attn_mask = torch.tril(torch.ones(T, T)).view(1,T,T).expand(B,T,T).to(device) 
+                attn_mask = attn_mask.unsqueeze(1).expand(B, self.n_head, T, T)
+
+            if attn_mask is not None:
+                att = att.masked_fill(attn_mask == 0, torch.finfo(att.dtype).min)
+
+            att = F.softmax(att, dim=-1)
+            att = self.attn_dropout(att)
+            y = att @ v 
+        y = y.transpose(1, 2).contiguous().view(B, T, C) 
+
+        y = self.resid_dropout(self.c_proj(y))
+
+        return y
+
+
+def init_elm_model(model_args=ModelArgs(), device="cuda", model_config_dict=None):
+    """Initialize ELM model using default or model_config parameters."""
+    if model_config_dict:
+        model_args = ModelArgs(**model_config_dict)
+
+    dtype = torch.bfloat16 if device=="cuda" and torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16
+
+    model = ELM(model_args=model_args).to(dtype=dtype)
+
+    return model
+
+def get_h_layers_in_ckpt(ckpt_state_dict, 
+                         layer_name_template = 'slice_transformer.h.{layer_num}.'):
+    num_layers_in_ckpt = 0
+    from collections import defaultdict
+    layer_wise_dict = defaultdict(lambda: defaultdict(list))
+    
+    layer_num_found = True
+    while layer_num_found:
+        layer_num_found = False
+        for layer_name in ckpt_state_dict.keys():
+            if layer_name_template.format(layer_num=num_layers_in_ckpt) in layer_name:
+                layer_wise_dict[num_layers_in_ckpt][layer_name] = ckpt_state_dict[layer_name]
+                layer_num_found = True
+        num_layers_in_ckpt += 1
+    return layer_wise_dict
+
+def load_elm_model_from_ckpt(ckpt_dir, device='cuda', load_partial=False, model_args=ModelArgs(), get_num_layers_from_ckpt=True):
+    """Load ELM model from local checkpoint."""
+    print(f"Loading ELM checkpoint from {ckpt_dir}")
+    ckpt_path = os.path.join(ckpt_dir, 'ckpt.pt')
+    checkpoint = torch.load(ckpt_path, map_location=device)
+
+    if get_num_layers_from_ckpt:
+        layer_name_template = 'slice_transformer.h.{layer_num}.'
+        ckpt_layer_wise_dict = get_h_layers_in_ckpt(checkpoint['model'], 
+                                                    layer_name_template = layer_name_template)
+        model_args.num_layers = len(ckpt_layer_wise_dict)
+    model = init_elm_model(model_args=model_args, device=device)
+    ckpt_state_dict = checkpoint['model']
+
+    unwanted_prefix = '_orig_mod.'
+    for k,v in list(ckpt_state_dict.items()):
+        if k.startswith(unwanted_prefix):
+            ckpt_state_dict[k[len(unwanted_prefix):]] = ckpt_state_dict.pop(k)
+
+    if load_partial:
+        mod_state_dict = model.state_dict()
+        for k,v in list(ckpt_state_dict.items()):
+            if k in mod_state_dict:
+                v_size = v.size()
+                mod_size = mod_state_dict[k].size()
+
+                if v_size == mod_size:
+                    mod_state_dict[k] = v
+                else:
+                    if len(v_size) == 1:
+                        mod_state_dict[k][:v_size[-1]] = v
+                    elif len(v_size) == 2:
+                        mod_state_dict[k][:v_size[-2], :v_size[-1]] = v
+
+        ckpt_state_dict = mod_state_dict
+    load_status = model.load_state_dict(ckpt_state_dict)
+    print(load_status)
+    model.to(device)
+
+    return model
+
+
+def sample_top_p(probs, threshold):
+    """Perform top-p sampling on probability distribution using a threshold."""
+    probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
+    probs_sum = torch.cumsum(probs_sort, dim=-1)
+    mask = probs_sum - probs_sort > threshold
+    probs_sort[mask] = 0.0
+    probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
+    next_token = torch.multinomial(probs_sort, num_samples=1)
+    next_token = torch.gather(probs_idx, -1, next_token)
+
+    return next_token

elm/positional_embeddings.py

@@ -0,0 +1,86 @@
+import torch
+from typing import Optional, Tuple
+
+
+def rotate_half(x):
+    x1, x2 = x.chunk(2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+@torch.jit.script
+def apply_rotary_pos_emb(x, cos, sin):
+    # NOTE: This could probably be moved to Triton
+
+    # Handle a possible sequence length mismatch in between q and k
+    cos = cos[:, :, : x.shape[-2], :]
+    sin = sin[:, :, : x.shape[-2], :]
+
+    return (x * cos) + (rotate_half(x) * sin)
+
+
+class RotaryEmbedding(torch.nn.Module):
+    """
+    Rotary position embeddings from RoFormer (Su et. al, 2021).
+    """
+
+    def __init__(self, dim_model: int, *_, **__):
+        super().__init__()
+        # Generate and save the inverse frequency buffer (non trainable)
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim_model, 2).float() / dim_model))
+        self.register_buffer("inv_freq", inv_freq)
+
+        self._seq_len_cached = None
+        self._cos_cached = None
+        self._sin_cached = None
+
+    def update_cos_sin_tables(self, x, seq_dimension=1):
+        seq_len = x.shape[seq_dimension]
+
+        # Reset the tables if the sequence length has changed,
+        # or if we're on a new device (possibly due to tracing for instance)
+        if (
+            seq_len != self._seq_len_cached
+            or self._cos_cached.device != x.device
+            or self._cos_cached.dtype != x.dtype
+        ):
+            self._seq_len_cached = seq_len
+            t = torch.arange(
+                x.shape[seq_dimension], device=x.device, dtype=torch.float32
+            )
+            freqs = torch.einsum("i,j->ij", t, self.inv_freq.to(x.dtype))
+            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+
+            self._cos_cached = emb.cos()[None, None, :, :].to(x.dtype)
+            self._sin_cached = emb.sin()[None, None, :, :].to(x.dtype)
+
+        return self._cos_cached, self._sin_cached
+
+    def forward(
+        self, q: torch.Tensor, k: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        self._cos_cached, self._sin_cached = self.update_cos_sin_tables(
+            k, seq_dimension=-2
+        )
+
+        return (
+            apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached),
+            apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached),
+        )
+
+
+def __test_rope__():
+    dtype=torch.float16
+    batch=4
+    seqlen=2048
+    dim=4096
+    num_heads=32
+    dim_key_head=dim // num_heads
+
+    x=torch.randn(batch,seqlen,num_heads,dim_key_head).to(dtype=dtype).to('cuda')
+
+    rpe=RotaryEmbedding(dim_key_head).to(dtype=dtype).to('cuda')
+    q,k=rpe(q=x,k=x)
+
+
+#__test_rope__()
+

elm/utils.py

@@ -0,0 +1,25 @@
+# Copyright (c) 2024, SliceX AI, Inc. All Rights Reserved.
+
+from prettytable import PrettyTable
+
+def count_parameters(model):
+    """Count the number of parameters in the model."""
+    table = PrettyTable(["Modules", "Parameters"])
+    total_params = 0
+
+    for name, parameter in model.named_parameters():
+        if not parameter.requires_grad: continue
+        params = parameter.numel()
+        table.add_row([name, params])
+        total_params+=params
+    
+    print(table)
+    print(f"Total Trainable Params: {total_params}")
+    
+    return total_params
+    
+
+def batchify(lst, n):
+    """Divide a list into chunks of size n."""
+    return [lst[i:i + n] for i in range(0, len(lst), n)]
+

models/.gitattributes

@@ -0,0 +1,2 @@
+“*.pt” filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text

models/elm-1.0_news_classification/added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "[PAD]": 50257
+}

models/elm-1.0_news_classification/ckpt.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e13e37f2410fff36cb11e2cd3cbcc814a380bdbecc06967a34c0d035d52294a3
+size 2124385874

models/elm-1.0_news_classification/example_prompts.json

@@ -0,0 +1,13 @@
+{
+    "inputs": [
+        "GM May Close Plant in Europe  DETROIT (Reuters) - General Motors Corp. <A HREF=\"http://www.investor.reuters.com/FullQuote.aspx?ticker=GM.N target=/stocks/quickinfo/fullquote\">GM.N</A> will likely  cut some jobs in Europe and may close a plant there as part of  a restructuring plan under development to try to return the  region to profitability, the U.S. automaker said on Wednesday.",
+        "Netflix, TiVo sign VoD alliance Netflix, the online DVD rental company, and TiVo yesterday said they will work together to deliver movies digitally down the wires, presumably specifically to the latter #39;s PVR equipment.",
+        "NBA Star Pippen Announces Retirement National Basketball Association star Scottie Pippen has announced his retirement from the game, leaving the Chicago Bulls team he helped lead to six NBA titles.",
+        "Radcliffe to Run in New York Marathon  LONDON (Reuters) - World marathon record holder Paula  Radcliffe believes she has put her failure at the Athens  Olympics behind her after announcing on Tuesday that she will  run in the New York marathon on November 7.",
+        "GE Says It's on Track for 2004, 2005  BOSTON (Reuters) - Diversified manufacturer General  Electric Co. <A HREF=\"http://www.investor.reuters.com/FullQuote.aspx?ticker=GE.N target=/stocks/quickinfo/fullquote\">GE.N</A> said on Tuesday that it is on track to meet  its full-year earnings forecast and to achieve double-digit  gains in earnings per share in 2005.",
+        "Hyundai signs deal for China truck plant Hyundai Motor Co. said yesterday that it has signed an agreement with a Chinese company, Jianghuai Automobile Corp., to build a commercial vehicle and engine plant in China #39;s Anhui province.",
+        "Sprint is chock full of potential heros It would be nice to see this week #39;s 100-meter sprint as simply the best footrace of all time. We could witness four sub-10-second sprints for the first time ever. It would be nice to watch with raised eyebrows instead of furrowed ones. It ...",
+        "Clash of the unpredictables: WI-Pak tie What would happen when two of the worlds most talented and unpredictable sides rub shoulders and that too in an ICC Champions Trophy semi-final?"
+    ],
+    "template": "[INST]Below is a news article. Please classify it under one of the following classes (World, Business, Sports, Sci/Tech). Please format your response as a JSON payload.\n\n### Article: {input}\n\n### JSON Response:[/INST]"
+}

models/elm-1.0_news_classification/special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

models/elm-1.0_news_classification/tokenizer_config.json

@@ -0,0 +1,30 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50256": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50257": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 1024,
+  "pad_token": "[PAD]",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}

requirements.txt

@@ -0,0 +1,2 @@
+torch
+transformers

run.py

@@ -0,0 +1,24 @@
+import os
+import argparse
+import json
+from elm.infer_elm import generate_elm_responses
+
+parser = argparse.ArgumentParser(description='run prompts with elm model.')
+parser.add_argument('elm_model_path', help='Path to the elm_model_path')
+
+
+def get_prompt_config_file(elm_model_path):
+    return os.path.join(elm_model_path, "example_prompts.json")
+
+def run(elm_model_path: str):
+    prompt_config_file = get_prompt_config_file(elm_model_path)
+    
+    with open(prompt_config_file, "r") as f:
+        prompt_info = json.load(f)
+    prompts = [prompt_info["template"].format(input=input) for input in prompt_info["inputs"]]
+    print(f"Loaded prompts from: {prompt_config_file}")
+    generate_elm_responses(elm_model_path, prompts, verbose=True)
+ 
+if __name__ == "__main__":
+    args = parser.parse_args()
+    run(args.elm_model_path)