utils.py

utils.py

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+import torch
+import random
+import torch.nn as nn
+import lightning as L
+from pathlib import Path
+from torch.utils.data import DataLoader
+from lightning.fabric.loggers import CSVLogger
+from lightning.fabric.strategies import FSDPStrategy
+
+from tsai_gpt.model import GPT, Block, Config
+from tsai_gpt.tokenizer import Tokenizer
+from tsai_gpt.utils import get_default_supported_precision, load_checkpoint, gptq_quantization
+
+model_name = "pythia-160m"
+name = "redpajama"
+
+checkpoint_dir = Path("iter-015000-ckpt.pth")
+quantize = None
+strategy = "auto"
+devices = 1
+precision = get_default_supported_precision(training=False)
+plugins = None
+fabric = L.Fabric(devices=devices, precision=precision, strategy=strategy, plugins=plugins)
+fabric.launch()
+
+example_text = [
+    "In the middle of the enchanted forest, there was a magical pond where the water shimmered with a faint glow of",
+    "The detective carefully examined the crime scene, searching for any clues that might lead to the identity of the",
+    "In the middle of the enchanted forest, there was a magical pond where the water shimmered with a faint glow of",
+    "The time machine malfunctioned, sending the protagonist to a dystopian future where robots had taken over and humans were forced to live underground to escape the threat of ",
+    "In the parallel universe, gravity worked differently, causing objects to float in the air as if affected by an invisible"
+]
+
+examples = [
+    [
+        text,
+        round(random.uniform(0.6, 0.9), 1),
+        round(int(random.uniform(120, 250)) / 10) * 10,
+        round(int(random.uniform(50, 100)) / 10) * 10,
+    ] for text in example_text
+]
+
+with fabric.init_module(empty_init=True), gptq_quantization(quantize=="gptq.int4"):
+    config = Config.from_name(model_name)
+    model = GPT(config)
+
+model.eval()
+model = fabric.setup_module(model)
+load_checkpoint(fabric, model, checkpoint_dir)
+
+tokenizer = Tokenizer(Path('tokenizer_files'))
+
+@torch.inference_mode()
+def generate(
+    model: GPT,
+    idx: torch.Tensor,
+    max_returned_tokens: int,
+    *,
+    temperature: float = 1.0,
+    top_k:int = None,
+    eos_id:int = None,
+) -> torch.Tensor:
+    """Takes a conditioning sequence (prompt) as input and continues to generate as many tokens as requested.
+    The implementation of this function is modified from A. Karpathy's nanoGPT.
+    Args:
+        model: The model to use.
+        idx: Tensor of shape (T) with indices of the prompt sequence.
+        max_returned_tokens: The maximum number of tokens to return (given plus generated).
+        temperature: Scales the predicted logits by 1 / temperature.
+        top_k: If specified, only sample among the tokens with the k highest probabilities.
+        eos_id: If specified, stop generating any more token once the <eos> token is triggered.
+    """
+    T = idx.size(0)
+    assert max_returned_tokens > T
+    if model.max_seq_length < max_returned_tokens - 1:
+        # rolling the kv cache based on the `input_pos` value would be necessary. However, doing so would introduce a
+        # data dependency on the `input_pos` tensor and impact model compilation. Since this setting is uncommon, we do
+        # not support it to avoid negatively impacting the overall speed
+        raise NotImplementedError(f"max_seq_length {model.max_seq_length} needs to be >= {max_returned_tokens - 1}")
+
+    device, dtype = idx.device, idx.dtype
+    # create an empty tensor of the expected final shape and fill in the current tokens
+    empty = torch.empty(max_returned_tokens, dtype=dtype, device=device)
+    empty[:T] = idx
+    idx = empty
+    input_pos = torch.arange(0, T, device=device)
+
+    # generate up to a fixed number of tokens
+    for _ in range(max_returned_tokens - T):
+        x = idx.index_select(0, input_pos).view(1, -1)
+
+        # forward
+        logits = model(x, input_pos)
+        logits = logits[0, -1] / temperature
+
+        # optionally crop the logits to only the top k options
+        if top_k is not None:
+            v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+            logits = torch.where(logits < v[[-1]], -float("Inf"), logits)
+
+        probs = torch.nn.functional.softmax(logits, dim=-1)
+        idx_next = torch.multinomial(probs, num_samples=1).to(dtype=dtype)
+
+        # advance
+        input_pos = input_pos[-1:] + 1
+
+        # concatenate the new generation
+        idx = idx.index_copy(0, input_pos, idx_next)
+
+        # if <eos> token is triggered, return the output (stop generation)
+        if idx_next == eos_id:
+            return idx[:input_pos]  # include the EOS token
+
+    return idx
+
+def generate_context(input_text, temperature, max_tokens, top_k):
+
+    encoded = tokenizer.encode(input_text, device=fabric.device)
+    
+    max_returned_tokens = encoded.size(0) + max_tokens
+
+    with fabric.init_tensor():
+        # set the max_seq_length to limit the memory usage to what we need
+        model.max_seq_length = max_returned_tokens
+        
+    with fabric.init_tensor():
+        model.set_kv_cache(batch_size=1)
+
+    y = generate(model, encoded, max_returned_tokens, temperature=temperature, top_k=top_k)
+
+    return(tokenizer.decode(y))