spoc-bot-vr/.ai.go

package main

import (
	"bytes"
	"context"
	"os"
	"strings"

	nn "github.com/nikolaydubina/llama2.go/exp/nnfast"
	"github.com/nikolaydubina/llama2.go/llama2"
	"github.com/tmc/langchaingo/llms"
	"github.com/tmc/langchaingo/llms/ollama"
)

type AI interface {
	Do(context.Context, string) (string, error)
}

type AINoop struct {
}

func NewAINoop() AINoop {
	return AINoop{}
}

func (ai AINoop) Do(ctx context.Context, prompt string) (string, error) {
	return ":shrug:", nil
}

type AIOllama struct {
	model string
	url   string
}

func NewAIOllama(url, model string) AIOllama {
	return AIOllama{url: url, model: model}
}

func (ai AIOllama) Do(ctx context.Context, prompt string) (string, error) {
	llm, err := ollama.New(
		ollama.WithModel(ai.model),
		ollama.WithServerURL(ai.url),
	)
	if err != nil {
		return "", err
	}
	return llms.GenerateFromSinglePrompt(ctx, llm, prompt)
}

type AILocal struct {
	checkpointPath string
	tokenizerPath  string
	temperature    float64
	steps          int
	topp           float64
}

func NewAILocal(
	checkpointPath string,
	tokenizerPath string,
	temperature float64,
	steps int,
	topp float64,
) AILocal {
	return AILocal{
		checkpointPath: checkpointPath,
		tokenizerPath:  tokenizerPath,
		temperature:    temperature,
		steps:          steps,
		topp:           topp,
	}
}

// https://github.com/nikolaydubina/llama2.go/blob/master/main.go
func (ai AILocal) Do(ctx context.Context, prompt string) (string, error) {
	checkpointFile, err := os.OpenFile(ai.checkpointPath, os.O_RDONLY, 0)
	if err != nil {
		return "", err
	}
	defer checkpointFile.Close()

	config, err := llama2.NewConfigFromCheckpoint(checkpointFile)
	if err != nil {
		return "", err
	}

	isSharedWeights := config.VocabSize > 0
	if config.VocabSize < 0 {
		config.VocabSize = -config.VocabSize
	}

	tokenizerFile, err := os.OpenFile(ai.tokenizerPath, os.O_RDONLY, 0)
	if err != nil {
		return "", err
	}
	defer tokenizerFile.Close()

	vocab := llama2.NewVocabFromFile(config.VocabSize, tokenizerFile)

	w := llama2.NewTransformerWeightsFromCheckpoint(config, checkpointFile, isSharedWeights)

	// right now we cannot run for more than config.SeqLen steps
	steps := ai.steps
	if steps <= 0 || steps > config.SeqLen {
		steps = config.SeqLen
	}

	runState := llama2.NewRunState(config)

	promptTokens := vocab.Encode(strings.ReplaceAll(prompt, "\n", "<0x0A>"))

	out := bytes.NewBuffer(nil)

	// the current position we are in
	var token int = 1 // 1 = BOS token in llama-2 sentencepiece
	var pos = 0

	for pos < steps {
		// forward the transformer to get logits for the next token
		llama2.Transformer(token, pos, config, runState, w)

		var next int
		if pos < len(promptTokens) {
			next = promptTokens[pos]
		} else {
			// sample the next token
			if ai.temperature == 0 {
				// greedy argmax sampling
				next = nn.ArgMax(runState.Logits)
			} else {
				// apply the temperature to the logits
				for q := 0; q < config.VocabSize; q++ {
					runState.Logits[q] /= float32(ai.temperature)
				}
				// apply softmax to the logits to the probabilities for next token
				nn.SoftMax(runState.Logits)
				// we now want to sample from this distribution to get the next token
				if ai.topp <= 0 || ai.topp >= 1 {
					// simply sample from the predicted probability distribution
					next = nn.Sample(runState.Logits)
				} else {
					// top-p (nucleus) sampling, clamping the least likely tokens to zero
					next = nn.SampleTopP(runState.Logits, float32(ai.topp))
				}
			}
		}
		pos++

		// data-dependent terminating condition: the BOS (1) token delimits sequences
		if next == 1 {
			break
		}

		// following BOS (1) token, sentencepiece decoder strips any leading whitespace
		var tokenStr string
		if token == 1 && vocab.Words[next][0] == ' ' {
			tokenStr = vocab.Words[next][1:]
		} else {
			tokenStr = vocab.Words[next]
		}
		out.Write([]byte(tokenStr))

		// advance forward
		token = next
	}
	out.Write([]byte("\n"))

	return strings.ReplaceAll(string(out.Bytes()), "<0x0A>", "\n"), nil
}