230 lines
9.5 KiB
Python
230 lines
9.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import os
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import math
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from typing import Iterable, List, Set, Tuple, Optional
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from collections.abc import Iterable
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import torch
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import torch.nn as nn
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from vllm.config import VllmConfig
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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 SamplerOutput, get_sampler
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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.layers.linear import ColumnParallelLinear
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm import _custom_ops as ops
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from vllm.distributed import tensor_model_parallel_all_gather, tensor_model_parallel_gather
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from vllm import envs
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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().__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-ai-platform and https://huggingface.co/ibm-granite
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"""
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
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super().__init__()
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self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
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config = vllm_config.model_config.hf_config
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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 > 1
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), "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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proj_first = ColumnParallelLinear(input_size=self.emb_dim,
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output_size=self.inner_dim,
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bias=False,
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gather_output=True)
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proj_tied = ColumnParallelLinear(input_size=self.inner_dim,
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output_size=self.inner_dim,
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bias=False,
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gather_output=True)
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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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ColumnParallelLinear(input_size=(self.emb_dim if i == 0 else self.inner_dim),
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output_size=self.inner_dim,
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bias=False,
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gather_output=True)
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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 = get_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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head_index: int
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) -> Tuple[Optional[SamplerOutput], Optional[torch.Tensor]]:
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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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if self.scale_input:
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previous_hidden_states = self.ln0(previous_hidden_states) / SQRT2
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# Project and predict
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z = self.emb[head_index](input_ids) # 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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# sampling_metadata is not None indicates that driver card is running
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if sampling_metadata is not None:
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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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return output, previous_hidden_states
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else:
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logits = self.head[head_index].linear_method.apply(self.head[head_index],
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states,
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bias=None)
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logits = tensor_model_parallel_gather(logits)
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return None, None
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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name = name.replace("speculator.", "")
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param = params_dict.get(name)
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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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loaded_params.add(name)
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if self.use_llama_nn or envs.VLLM_USE_NN:
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if (os.environ['LM_NN'] == '1' and "head" in name) or "proj" in name:
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_weight = torch.zeros_like(param.data)
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ori_shape =_weight.shape
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ops.trans_w16_gemm(_weight, param.data, _weight.shape[0], _weight.shape[1])
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param.data.copy_(_weight)
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param.data=param.data.reshape(ori_shape[1],-1)
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return loaded_params
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