temp last

import torch
from exllamav2 import ExLlamaV2Tokenizer
from exllamav2.ext import exllamav2_ext as ext_c, none_tensor
import os

class ExLlamaV2Sampler:

    class Settings:

        token_repetition_penalty = 1.15
        token_repetition_range = -1
        token_repetition_decay = 0

        temperature = 0.9
        top_k = 40
        top_p = 0.9
        min_p = 0
        tfs = 0
        typical = 0

        temperature_last = False

        mirostat = False
        mirostat_tau = 1.5
        mirostat_eta = 0.1
        mirostat_mu = None  # (re)initialized from mirostat_tau on first sample

        token_bias = None

        filters = []


        def clone(self):

            c = ExLlamaV2Sampler.Settings()

            c.token_repetition_penalty = self.token_repetition_penalty
            c.token_repetition_range = self.token_repetition_range
            c.token_repetition_decay = self.token_repetition_decay

            c.temperature = self.temperature
            c.top_k = self.top_k
            c.top_p = self.top_p
            c.min_p = self.min_p
            c.tfs = self.tfs
            c.typical = self.typical

            c.mirostat = self.mirostat
            c.mirostat_tau = self.mirostat_tau
            c.mirostat_eta = self.mirostat_eta
            c.mirostat_mu = None if self.mirostat_mu is None else self.mirostat_mu.copy()

            c.token_bias = self.token_bias
            c.filters = [f.clone() for f in self.filters]

            return c


        def greedy_clone(self):

            c = ExLlamaV2Sampler.Settings()
            c.top_k = 1
            c.top_p = 0
            c.token_repetition_penalty = self.token_repetition_penalty
            c.token_repetition_range = self.token_repetition_range
            c.token_repetition_decay = self.token_repetition_decay
            c.token_bias = self.token_bias
            c.filters = []
            return c


        def disallow_tokens(self, tokenizer, tokens):

            if self.token_bias is None:
                padding = -tokenizer.config.vocab_size % 32
                self.token_bias = torch.zeros((tokenizer.config.vocab_size + padding,), dtype = torch.float)

            self.token_bias[tokens] = float("-inf")


        def begin_filters(self, prefix_str = ""):

            for f in self.filters: f.begin(prefix_str)


        def feed_filters(self, feed_token):

            for f in self.filters: f.feed(feed_token)


    @staticmethod
    def sample(logits: torch.tensor, settings: Settings, sequence_ids: torch.tensor, random: float, tokenizer: ExLlamaV2Tokenizer, prefix_token = None):

        batch_size, _, vocab_size = logits.shape

        assert logits.shape[1] == 1, "Logits tensor is incorrect shape, must be (bsz, 1, vocab_size)"
        assert prefix_token is None or prefix_token.shape == (batch_size, 1), "Prefix token list doesn't match batch shape"
        assert batch_size == 1 or len(settings.filters) == 0, "Filters not implemented for batch size > 1"

        logits = logits.clone().squeeze(1)
        logit_filter = torch.ones((batch_size, vocab_size), dtype = torch.bool)

        # Repetition penalty

        if settings.token_repetition_penalty != 1.0:

            ext_c.apply_rep_penalty(sequence_ids,
                                    settings.token_repetition_penalty,
                                    settings.token_repetition_range,
                                    settings.token_repetition_decay,
                                    logits)

        # Token bias

        if settings.token_bias is not None: logits += settings.token_bias

        # Evaluate filters

        if len(settings.filters) > 0:

            pass_tokens = None
            end_tokens = None
            for f in settings.filters:

                pt, et = f.next()
                pass_tokens = pt if pass_tokens is None else pass_tokens & pt
                end_tokens = et if end_tokens is None else end_tokens | et

            assert pass_tokens, "Filter excluded all tokens"
            ext_c.logit_filter_exclusive(logit_filter, [sorted(list(pass_tokens))])

        # Healing

        if prefix_token is not None:

            prefix_id_to_ids = tokenizer.get_prefix_id_to_ids_dict()

            valid_token_lists = []
            for i in range(batch_size):
                valid_token_lists.append(prefix_id_to_ids[prefix_token[i, 0].item()])

            ext_c.logit_filter_exclusive(logit_filter, valid_token_lists)

        # for i in range(logit_filter.shape[-1]):
        #     if logit_filter[0, i].item():
        #         print(i)

        # Begin Mirostat

        if settings.mirostat:
            if settings.mirostat_mu is None:
                settings.mirostat_mu = [0.0] * batch_size

        # Sampling

        batch_size = logits.shape[0]

         # This is a goddamn mess because I was too stubborn to modify the C++ for accessibility's sake. Works though.

        def apply_top_k_top_p(logits, top_k, top_p):
            # If top_k is set (and greater than 0), we filter out all but the top k logits
            if top_k > 0:
                top_values, _ = logits.topk(top_k, dim=-1)

                # Create a mask to identify logits that are not in the top k
                mask = torch.ones_like(logits, dtype=torch.bool)
                mask.scatter_(1, _, False)

                # Zero out all logits that are not in the top k
                logits[mask] = -float('Inf')

            # If top_p is set (and less than 1), we filter out all but the top p probability mass
            if top_p > 0 and top_p < 1.0:
                # Sort the logits so they are in descending order for cumulative probability calculation
                sorted_logits, sorted_indices = torch.sort(logits, descending=True)

                # Convert logits to probabilities
                cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)

                # Create a mask for values to be removed, i.e., where cumulative probability > top_p
                remove_mask = cumulative_probs > top_p

                # Since we want to keep the first token that exceeds top_p, shift the mask one place to the right
                remove_mask[..., 1:] = remove_mask[..., :-1].clone()
                remove_mask[..., 0] = 0

                # Apply the mask to the sorted indices, find the original indices to be removed, and zero out the corresponding logits
                indices_to_remove = sorted_indices[remove_mask]
                logits.view(-1)[indices_to_remove.view(-1)] = -float('Inf')

            # Extract the valid logits (those that are not '-Inf' indicating they've been filtered out)
            valid_logits = logits[logits != -float('Inf')]

            return valid_logits  # Now we're returning only the valid logits

        #print("-----------------------")

        # Check if the original temperature is within the specified range
        if 1.83 <= settings.temperature <= 1.84:
            # Apply the Top-K and Top-P sampling to get valid logits
            valid_logits = apply_top_k_top_p(logits.clone(), settings.top_k, settings.top_p)

            # Calculate the entropy only on the valid logits
            probabilities = torch.softmax(valid_logits, dim=-1)  # Converts valid logits to probabilities.
            entropy = -torch.sum(probabilities * torch.log(probabilities + 1e-10), dim=-1)

            # Check if the entropy is negative zero, and if so, set it to zero
            if entropy.eq(-0.0).any():
                entropy = torch.zeros_like(entropy)

            #print("Entropy:", entropy.item())

            # Calculate the maximum possible entropy based on the number of valid logits
            max_entropy = torch.log(torch.tensor(float(valid_logits.size(-1))))

            #print("Max Possible Entropy:", max_entropy.item())

            # If the maximum entropy is zero (which happens if there's only one valid logit),
            # then the normalized entropy should directly be zero, as there's no uncertainty.
            if max_entropy.eq(0).any():
                normalized_entropy = torch.zeros_like(entropy)
            else:
                normalized_entropy = torch.div(entropy, max_entropy)  # Normalize entropy values.

            #print("Normalized Entropy:", normalized_entropy.item())

            # Check if the normalized entropy is 'nan' (which can happen if max_entropy is zero), and if so, set it to zero
            if torch.isnan(normalized_entropy):
                normalized_entropy = torch.zeros_like(normalized_entropy)

            min_temp = 0.0
            max_temp = 2.0

            # Define the file name
            file_name = "EntropySampling.txt"

            # Check if the file exists. If not, create it with default values.
            if not os.path.exists(file_name):
                with open(file_name, 'w') as file:
                    file.write("min_temp=0.0\nmax_temp=2.0\n")

            # Read the values from the file
            with open(file_name, 'r') as file:
                lines = file.readlines()
                min_temp = float(lines[0].split('=')[1])
                max_temp = float(lines[1].split('=')[1])

            #print("min_temp:", min_temp)
            #print("max_temp:", max_temp)

            # Calculate the dynamic temperature based on normalized entropy.
            # We use a simple linear mapping.
            dynamic_temperature = min_temp + (max_temp - min_temp) * normalized_entropy  # Linear scaling.

            if dynamic_temperature == 0:
                dynamic_temperature = torch.full_like(dynamic_temperature, 0.00390625)  # fill with 1/256th of 1 because exllama cries otherwise for some reason

            #print("Dynamic Temperature (dyn_temp):", dynamic_temperature.item())

            # Ensure the dynamic temperature is within the defined min/max range.
            dynamic_temperature = torch.clamp(dynamic_temperature, min=min_temp, max=max_temp)
        else:
            # If the temperature is not in the specified range, use the original temperature
            #print("Temperature was not set to 1.84 override value, using static temp instead:", settings.temperature)
            dynamic_temperature = settings.temperature

        #print("-----------------------")

        # Sampling step using the dynamic temperature.

        output_tokens = torch.empty((batch_size, 1), device = "cpu", dtype = torch.long)
        output_probs = torch.empty((batch_size, 1), device = "cpu", dtype = torch.float)

        m = ext_c.sample_basic(logits,
                               #dynamic_temperature,  # If override isn't on, this is just reg temp.
                               #1.0 if settings.temperature_last else settings.temperature,
                               settings.top_k,
                               settings.top_p,
                               settings.min_p,
                               settings.tfs,
                               settings.typical,
                               random,
                               output_tokens,
                               output_probs,
                               logit_filter,
                               settings.mirostat,
                               settings.mirostat_mu if settings.mirostat else [],
                               settings.mirostat_tau,
                               settings.mirostat_eta,
                               #settings.temperature if settings.temperature_last else 1.0)
                               dynamic_temperature)

        if settings.mirostat: settings.mirostat_mu = m

        # Stop condition from filters

        end_filter = False
        if len(settings.filters) > 0 and output_tokens[0].item() in end_tokens: end_filter = True

        return output_tokens, output_probs, end_filter