separated samplers into its own file, don't bother copying the logits back to the GPU after sampling, it's not necessary
This commit is contained in:
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@ -201,7 +201,7 @@ class AR(Base):
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for i, ri in enumerate(r):
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if self.stop_token in ri:
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stopped[i] = True
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sequence_list[i] = torch.cat([sequence_list[i], ri])
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sequence_list[i] = torch.cat([sequence_list[i], ri.to(device)])
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# stop token found
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stopped |= r == self.stop_token
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@ -120,8 +120,6 @@ class AR_NAR(Base):
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if cfg.experimental:
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proms_list = [ r if l == 0 else trim(r, 75 * 3) for r, l in zip(proms_list, quant_levels) ] # trim input prompt to 3 seconds
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#quant_levels.to(device=device)
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return super().forward(
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text_list=text_list,
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proms_list=proms_list,
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@ -140,7 +138,7 @@ class AR_NAR(Base):
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if level >= max_levels + 1: # min(max_levels + 1, self.n_resp_levels): # commented out to experiment with exceeding trained levels
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break
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quant_levels = torch.full((len(text_list),), level, device=device)
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quant_levels = torch.full((len(text_list),), level)
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logits = super().forward(
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text_list=text_list,
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@ -165,7 +163,7 @@ class AR_NAR(Base):
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#mirostat=mirostat,
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)
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prev_list = [ torch.cat([rs, r.unsqueeze(-1)], dim=-1) for rs, r in zip(prev_list, resps_list) ]
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prev_list = [ torch.cat([rs, r.unsqueeze(-1).to(device)], dim=-1) for rs, r in zip(prev_list, resps_list) ]
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return prev_list
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@ -238,7 +236,7 @@ class AR_NAR(Base):
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for i, ri in enumerate(r):
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if self.stop_token in ri:
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stopped[i] = True
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sequence_list[i] = torch.cat([sequence_list[i], ri])
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sequence_list[i] = torch.cat([sequence_list[i], ri.to(device)])
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# stop token found
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stopped |= r == self.stop_token
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@ -16,6 +16,7 @@ from torchmetrics.classification import BinaryAccuracy, MulticlassAccuracy, Mult
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from .retnet import RetNetDecoder, RetNetConfig
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from .transformer import SinusoidalEmbedding, Block as TransformerBlock
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from ..samplers import reptition_penalize, length_penalize, top_k_top_p_filtering, dynamic_temperature, top_k_logits_list, mirostat_sample
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def _create_mask(l, device):
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"""1 is valid region and 0 is invalid."""
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@ -50,159 +51,6 @@ def list_to_tensor(x_list: list[Tensor], pattern="t b c -> b t c"):
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m = m.to(x)
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return x, m
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# Simple filter to modify a token's probability if it shows up in the past
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# `one_time` will only apply the penalty once
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# `decay` is a factor that will exponentially apply to how far away it is
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def reptition_penalize( logits, previous, factor=1.0, decay=0.0, one_time=True ):
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if factor == 1.0 or previous is None:
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return logits
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unique = set()
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priors = reversed(previous.tolist())
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for distance, token in enumerate(priors):
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# skip if we're only applying the decay once
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if one_time and token in unique:
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continue
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distance += 1
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logits[:, token] /= factor * (distance ** decay)
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# add to set if we care about it
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if one_time:
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unique.add(token)
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return logits
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# Simple "filter" that modifies the logit for the stop token, based on the sequence length
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# `length` is the length of the sequence currently
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# `factor` is the power the length is raised to, so values > 0 will yield longer sequences, values < 0 will yield shorter sequences
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# `token` is the stop token.
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def length_penalize( logits, length, factor=0.0, token=-1 ):
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if factor == 0.0:
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return logits
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logits[:, token] /= (length ** factor)
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return logits
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# Credit to https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py#L1145 / https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
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def top_k_top_p_filtering( logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens=1 ):
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"""Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
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Args:
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logits: logits distribution shape (batch size, vocabulary size)
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if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
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if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
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Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
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Make sure we keep at least min_tokens per batch example in the output
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"""
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if top_k > 0:
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top_k = min(max(top_k, min_tokens), logits.size(-1)) # Safety check
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# Remove all tokens with a probability less than the last token of the top-k
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indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
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logits[indices_to_remove] = filter_value
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if top_p < 1.0:
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sorted_logits, sorted_indices = torch.sort(logits, descending=True)
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cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
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# Remove tokens with cumulative probability above the threshold (token with 0 are kept)
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sorted_indices_to_remove = cumulative_probs > top_p
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if min_tokens > 1:
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# Keep at least min_tokens (set to min_tokens-1 because we add the first one below)
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sorted_indices_to_remove[..., :min_tokens] = 0
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# Shift the indices to the right to keep also the first token above the threshold
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sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
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sorted_indices_to_remove[..., 0] = 0
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# scatter sorted tensors to original indexing
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indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
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logits[indices_to_remove] = filter_value
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return logits
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# credit to https://github.com/LostRuins/koboldcpp/pull/464 // https://github.com/kalomaze/koboldcpp/tree/dynamic-temp
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def dynamic_temperature( logits, temperature=1.0, min_temperature = 0.0, k = 10, sigmoidCenterPoint = 0.5 ):
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# loop over logits[:], as the NAR will have logits.shape[0] > 1
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for i in range(logits.shape[0]):
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sum_exp = 0.0
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maximum = torch.max( logits[i] )
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for logit in logits[i]:
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sum_exp += math.exp( logit - maximum )
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prob_max_token_before_temp = 1.0 / sum_exp
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dynamic_temperature = temperature - (temperature - min_temperature) / (1 + math.exp(-k * (prob_max_token_before_temp - sigmoidCenterPoint)))
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logits[i] /= dynamic_temperature
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return logits
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# picks the top K tokens amongst a batch of logits
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# logits: [Tensor] list of logits
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# candidates: [(batch, token)] list, where batch indicates the index of the logits the given token is from
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def top_k_logits_list( logits_list, k ):
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# ( batch, tokens ) => ( batch x tokens )
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logits = torch.cat( logits_list )
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candidates = list(torch.topk(logits.flatten(), k).indices.tolist()) # perform top-k across all logits
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for i, index in enumerate(candidates):
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t = []
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N = np.prod(logits.size())
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for n in logits.size():
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N //= n
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t.append(index // N)
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index %= N
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candidates[i] = tuple(t)
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return candidates
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# Credit to: https://github.com/basusourya/mirostat/
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# performs mirostat-based sampling
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# logits: Tensor of logit probabilities
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# state: the mirostat state
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def mirostat_sample( logits, state = None ):
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def compute_k(prob, n, tau):
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num = 0
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den = 0
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for i in range(100):
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b = prob[i]/prob[i+1]
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t = (i+2)/(i+1)
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num += math.log(b)*math.log(t)
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den += math.log(t)**2
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s = num/den
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eps = s-1
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k = ((eps*(2**(tau)))/(1-n**(-eps)))**(1/s)
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k = round(k)
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return k
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if "max_surprise" not in state:
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state["max_surprise"] = state["tau"] * 2
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if "error_surprise" not in state:
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state["error_surprise"] = 0
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if "running_total_surprise" not in state:
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state["running_total_surprise"] = 0
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sorted_logits, sorted_indices = torch.sort( logits[-1, :], descending=True )
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prob_original = torch.softmax( sorted_logits, dim=-1 ).tolist()
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k = compute_k(prob_original, state["n"], state["max_surprise"]) + 1
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sorted_logits = sorted_logits[0:k]
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sorted_indices = sorted_indices[0:k]
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prob_topk = torch.softmax(sorted_logits, dim = 0)
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prev_i = torch.multinomial(prob_topk, num_samples=1, replacement=True)
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state["index_surprise"] = math.log2(1/prob_original[prev_i])
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state["running_total_surprise"] += state["index_surprise"]
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state["error_surprise"] = state["index_surprise"] - state["tau"]
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state["max_surprise"] -= state["eta"] * state["error_surprise"]
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state["token"] = sorted_indices[prev_i]
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return state
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# automagically parses a batch-list and returns it as a list
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class Embedding(nn.Embedding):
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def forward(self, x_list: list[Tensor]) -> list[Tensor]:
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@ -594,12 +442,12 @@ class Base(nn.Module):
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# to-do: not naively implement beam searching
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if beam_width > 1:
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candidates = top_k_logits_list( logits, beam_width )
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res = [ torch.tensor(token, device=logits[batch].device, dtype=torch.int16).unsqueeze(dim=-1) for batch, token in candidates ]
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scores = [ logits[batch].flatten()[token].to(logits[batch].device) for batch, token in candidates ]
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res = [ torch.tensor(token, dtype=torch.int16).unsqueeze(dim=-1) for batch, token in candidates ]
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scores = [ logits[batch].flatten()[token] for batch, token in candidates ]
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return res, scores
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# and sample
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return [ Categorical(logits=logit).sample().to(device) for logit, device in zip(logits, devices) ]
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return [ Categorical(logits=logit).sample() for logit in logits ]
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def example_usage():
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from ..config import cfg
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@ -159,7 +159,7 @@ class NAR(Base):
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#mirostat_state=mirostat_state,
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)
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prev_list = [ torch.cat([rs, r.unsqueeze(-1)], dim=-1) for rs, r in zip(prev_list, resps_list) ]
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prev_list = [ torch.cat([rs, r.unsqueeze(-1).to(device)], dim=-1) for rs, r in zip(prev_list, resps_list) ]
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return prev_list
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158
vall_e/samplers.py
Normal file
158
vall_e/samplers.py
Normal file
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@ -0,0 +1,158 @@
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import math
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import torch
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import torch.nn.functional as F
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import numpy as np
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from torch import Tensor, einsum, nn
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# Simple filter to modify a token's probability if it shows up in the past
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# `one_time` will only apply the penalty once
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# `decay` is a factor that will exponentially apply to how far away it is
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def reptition_penalize( logits, previous, factor=1.0, decay=0.0, one_time=True ):
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if factor == 1.0 or previous is None:
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return logits
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unique = set()
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priors = reversed(previous.tolist())
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for distance, token in enumerate(priors):
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# skip if we're only applying the decay once
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if one_time and token in unique:
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continue
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distance += 1
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logits[:, token] /= factor * (distance ** decay)
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# add to set if we care about it
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if one_time:
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unique.add(token)
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return logits
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# Simple "filter" that modifies the logit for the stop token, based on the sequence length
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# `length` is the length of the sequence currently
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# `factor` is the power the length is raised to, so values > 0 will yield longer sequences, values < 0 will yield shorter sequences
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# `token` is the stop token.
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def length_penalize( logits, length, factor=0.0, token=-1 ):
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if factor == 0.0:
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return logits
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logits[:, token] /= (length ** factor)
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return logits
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# Credit to https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py#L1145 / https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
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def top_k_top_p_filtering( logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens=1 ):
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"""Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
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Args:
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logits: logits distribution shape (batch size, vocabulary size)
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if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
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if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
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Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
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Make sure we keep at least min_tokens per batch example in the output
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"""
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if top_k > 0:
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top_k = min(max(top_k, min_tokens), logits.size(-1)) # Safety check
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# Remove all tokens with a probability less than the last token of the top-k
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indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
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logits[indices_to_remove] = filter_value
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if top_p < 1.0:
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sorted_logits, sorted_indices = torch.sort(logits, descending=True)
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cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
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# Remove tokens with cumulative probability above the threshold (token with 0 are kept)
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sorted_indices_to_remove = cumulative_probs > top_p
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if min_tokens > 1:
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# Keep at least min_tokens (set to min_tokens-1 because we add the first one below)
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sorted_indices_to_remove[..., :min_tokens] = 0
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# Shift the indices to the right to keep also the first token above the threshold
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sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
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sorted_indices_to_remove[..., 0] = 0
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# scatter sorted tensors to original indexing
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indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
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logits[indices_to_remove] = filter_value
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return logits
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# credit to https://github.com/LostRuins/koboldcpp/pull/464 // https://github.com/kalomaze/koboldcpp/tree/dynamic-temp
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def dynamic_temperature( logits, temperature=1.0, min_temperature = 0.0, k = 10, sigmoidCenterPoint = 0.5 ):
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# loop over logits[:], as the NAR will have logits.shape[0] > 1
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for i in range(logits.shape[0]):
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sum_exp = 0.0
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maximum = torch.max( logits[i] )
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for logit in logits[i]:
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sum_exp += math.exp( logit - maximum )
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prob_max_token_before_temp = 1.0 / sum_exp
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dynamic_temperature = temperature - (temperature - min_temperature) / (1 + math.exp(-k * (prob_max_token_before_temp - sigmoidCenterPoint)))
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logits[i] /= dynamic_temperature
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return logits
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# picks the top K tokens amongst a batch of logits
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# logits: [Tensor] list of logits
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# candidates: [(batch, token)] list, where batch indicates the index of the logits the given token is from
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def top_k_logits_list( logits_list, k ):
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# ( batch, tokens ) => ( batch x tokens )
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logits = torch.cat( logits_list )
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candidates = list(torch.topk(logits.flatten(), k).indices.tolist()) # perform top-k across all logits
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for i, index in enumerate(candidates):
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t = []
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N = np.prod(logits.size())
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for n in logits.size():
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N //= n
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t.append(index // N)
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index %= N
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candidates[i] = tuple(t)
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return candidates
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# Credit to: https://github.com/basusourya/mirostat/
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# performs mirostat-based sampling
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# logits: Tensor of logit probabilities
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# state: the mirostat state
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def mirostat_sample( logits, state = None ):
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def compute_k(prob, n, tau):
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num = 0
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den = 0
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for i in range(100):
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b = prob[i]/prob[i+1]
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t = (i+2)/(i+1)
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num += math.log(b)*math.log(t)
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den += math.log(t)**2
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s = num/den
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eps = s-1
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k = ((eps*(2**(tau)))/(1-n**(-eps)))**(1/s)
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k = round(k)
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return k
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if "max_surprise" not in state:
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state["max_surprise"] = state["tau"] * 2
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if "error_surprise" not in state:
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state["error_surprise"] = 0
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if "running_total_surprise" not in state:
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state["running_total_surprise"] = 0
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sorted_logits, sorted_indices = torch.sort( logits[-1, :], descending=True )
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prob_original = torch.softmax( sorted_logits, dim=-1 ).tolist()
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k = compute_k(prob_original, state["n"], state["max_surprise"]) + 1
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sorted_logits = sorted_logits[0:k]
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sorted_indices = sorted_indices[0:k]
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prob_topk = torch.softmax(sorted_logits, dim = 0)
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prev_i = torch.multinomial(prob_topk, num_samples=1, replacement=True)
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state["index_surprise"] = math.log2(1/prob_original[prev_i])
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state["running_total_surprise"] += state["index_surprise"]
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||||
state["error_surprise"] = state["index_surprise"] - state["tau"]
|
||||
state["max_surprise"] -= state["eta"] * state["error_surprise"]
|
||||
state["token"] = sorted_indices[prev_i]
|
||||
|
||||
return state
|
||||
Loading…
Reference in New Issue
Block a user