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198 lines
7.5 KiB
Python
198 lines
7.5 KiB
Python
import math
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from typing import Iterable, List, Tuple
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import torch
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import torch.nn as nn
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from vllm.model_executor import SamplingMetadata
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.transformers_utils.configs import MLPSpeculatorConfig
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SQRT2 = 2**0.5
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class MLPSpeculatorLayerNorm(nn.Module):
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"""
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A L2 normalization implementation
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...
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Args
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----
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normalized_shape : int
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Dimensionality of input data (size of final tensor axis)
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eps : float
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Safety term to prevent division by zero. Make sure the chosen value
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fits in the range of your encoding scheme
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(i.e. fp16 requires eps >= 6e-8).
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elementwise_scale_and_shift : bool
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Include a learned scaling and shift term after normalization.
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"""
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def __init__(
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self,
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normalized_shape,
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eps=1e-06,
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elementwise_scale_and_shift=True,
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):
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super(MLPSpeculatorLayerNorm, self).__init__()
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self.elementwise_scale_and_shift = elementwise_scale_and_shift
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if self.elementwise_scale_and_shift:
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self.weight = nn.Parameter(torch.empty(normalized_shape))
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self.bias = nn.Parameter(torch.empty(normalized_shape))
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self.eps = eps
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def forward(self, x):
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xf = x
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xf = xf * torch.rsqrt(xf.pow(2).mean(-1, keepdim=True) + self.eps)
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x = xf.type_as(x)
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if self.elementwise_scale_and_shift:
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x = self.weight * x
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x = x + self.bias
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return x
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class MLPSpeculator(nn.Module):
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"""
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An implementation of the speculative models introduced in
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"Accelerating Production LLMs with Combined Token/Embedding
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Speculators"
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https://arxiv.org/pdf/2404.19124
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Trained speculators of this type are available on HF hub at:
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https://huggingface.co/ibm-fms and https://huggingface.co/ibm-granite
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"""
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def __init__(self, config: MLPSpeculatorConfig, **kwargs) -> None:
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super().__init__()
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self.n_predict = config.n_predict
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self.vocab_size = config.vocab_size
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self.emb_dim = config.emb_dim
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self.inner_dim = config.inner_dim if config.inner_dim != 0 \
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else config.emb_dim
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self.max_speculative_tokens = config.num_lookahead_tokens
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self.tie_weights = config.tie_weights
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self.scale_input = config.scale_input
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if self.tie_weights:
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assert (
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self.n_predict >
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1), "You cannot tie weights between stages when only 1 exists"
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embedding = VocabParallelEmbedding(
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config.vocab_size,
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self.inner_dim,
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org_num_embeddings=config.vocab_size)
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self.emb = nn.ModuleList([embedding] * self.max_speculative_tokens)
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# the initial projection from the base model may
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# have a different size, so that stays separate.
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proj_first = nn.Linear(self.emb_dim, self.inner_dim, bias=False)
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proj_tied = nn.Linear(self.inner_dim, self.inner_dim, bias=False)
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self.proj = nn.ModuleList([proj_first] + [proj_tied] *
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(self.max_speculative_tokens - 1))
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head = ParallelLMHead(self.vocab_size, self.inner_dim, bias=False)
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self.head = nn.ModuleList([head] * self.max_speculative_tokens)
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ln = MLPSpeculatorLayerNorm(self.inner_dim,
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elementwise_scale_and_shift=True)
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self.ln = nn.ModuleList([ln] * self.max_speculative_tokens)
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else:
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self.emb = nn.ModuleList([
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VocabParallelEmbedding(config.vocab_size,
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self.inner_dim,
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org_num_embeddings=config.vocab_size)
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for _ in range(self.max_speculative_tokens)
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])
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self.proj = nn.ModuleList([
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nn.Linear((self.emb_dim if i == 0 else self.inner_dim),
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self.inner_dim,
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bias=False)
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for i in range(self.max_speculative_tokens)
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])
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self.head = nn.ModuleList([
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ParallelLMHead(self.vocab_size, self.inner_dim, bias=False)
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for _ in range(self.max_speculative_tokens)
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])
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self.ln = nn.ModuleList([
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MLPSpeculatorLayerNorm(self.inner_dim,
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elementwise_scale_and_shift=True)
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for _ in range(self.max_speculative_tokens)
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])
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if self.scale_input:
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self.ln0 = MLPSpeculatorLayerNorm(
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self.emb_dim, elementwise_scale_and_shift=False)
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self.state_weight = 0.5**(0.5 / config.n_predict)
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self.emb_weight = math.sqrt(
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(1 - self.state_weight**2) * (self.inner_dim / 2))
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self.activation = nn.GELU()
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self.config = config
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self.logits_processor = LogitsProcessor(config.vocab_size,
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config.vocab_size, 1.0)
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self.sampler = Sampler()
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def generate_proposals(
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self,
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input_ids: torch.Tensor,
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previous_hidden_states: torch.Tensor,
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num_predict_tokens: int,
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sampling_metadata: SamplingMetadata,
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) -> List[SamplerOutput]:
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if num_predict_tokens > self.max_speculative_tokens:
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raise ValueError(f"Max speculative tokens for model is "
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f"{self.max_speculative_tokens}, but "
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f"{num_predict_tokens} were requested")
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# b x 1 x d
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previous_hidden_states = previous_hidden_states.unsqueeze(1)
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if self.scale_input:
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previous_hidden_states = self.ln0(previous_hidden_states) / SQRT2
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# b x 1
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last_tokens = input_ids.unsqueeze(1)
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next_tokens = []
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for head_index in range(num_predict_tokens):
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# Project and predict
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z = self.emb[head_index](last_tokens) # b k d
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states = self.proj[head_index](previous_hidden_states)
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# Weighted add of state_weight*state and emb_weight*z
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# Let subsequent LN take care of denominator
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# state_weight is close to 1, so shouldn't be any precision issues
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states.add_(z, alpha=self.emb_weight / self.state_weight)
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states = self.activation(self.ln[head_index](states)) # b k d
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previous_hidden_states = states
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# TODO: not yet supporting top_k_tokens_per_head
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states = states.flatten(0, 1)
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logits = self.logits_processor(self.head[head_index], states,
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sampling_metadata)
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output = self.sampler(logits, sampling_metadata)
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last_tokens = output.sampled_token_ids
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next_tokens.append(output)
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return next_tokens
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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param = params_dict.get(name.replace("speculator.", ""))
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if param is not None:
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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