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508 lines
20 KiB
Python
508 lines
20 KiB
Python
# Adapted from
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# https://github.com/huggingface/transformers/blob/a5cc30d72ae2dc19af534e4b35c986cc28db1275/src/transformers/models/falcon/modeling_falcon.py
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# Copyright 2023 The vLLM team.
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# Copyright 2023 the Falcon authors and HuggingFace Inc. team. All rights
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# reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch Falcon model."""
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import math
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from typing import Iterable, List, Optional, Tuple, Union
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import torch
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from torch import nn
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from torch.nn import LayerNorm
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from transformers import FalconConfig as HF_FalconConfig
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from vllm.attention import Attention, AttentionMetadata
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig
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from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce)
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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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.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.configs import RWConfig
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from .interfaces import SupportsPP
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from .utils import (is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers)
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FalconConfig = Union[HF_FalconConfig, RWConfig]
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def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
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closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
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base = torch.tensor(2**(-(2**-(math.log2(closest_power_of_2) - 3))),
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dtype=torch.float32)
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powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
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slopes = torch.pow(base, powers)
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if closest_power_of_2 != total_num_heads:
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extra_base = torch.tensor(
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2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
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dtype=torch.float32)
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num_remaining_heads = min(closest_power_of_2,
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total_num_heads - closest_power_of_2)
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extra_powers = torch.arange(1,
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1 + 2 * num_remaining_heads,
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2,
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dtype=torch.int32)
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slopes = torch.cat(
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[slopes, torch.pow(extra_base, extra_powers)], dim=0)
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return slopes
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class FalconAttention(nn.Module):
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def __init__(
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self,
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config: FalconConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.hidden_size = config.hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.head_dim = self.hidden_size // self.total_num_heads
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assert self.head_dim * self.total_num_heads == self.hidden_size
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self.new_decoder_architecture = config.new_decoder_architecture
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self.multi_query = config.multi_query
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if self.new_decoder_architecture:
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self.total_num_kv_heads = config.num_kv_heads
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elif self.multi_query:
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self.total_num_kv_heads = 1
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else:
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self.total_num_kv_heads = self.total_num_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.query_key_value = QKVParallelLinear(
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self.hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=config.bias,
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skip_bias_add=True,
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quant_config=quant_config,
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)
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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# Layer-wise attention scaling
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self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
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self.reduce_row_parallel_results = not (config.new_decoder_architecture
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or config.parallel_attn)
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self.dense = RowParallelLinear(
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self.hidden_size,
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self.hidden_size,
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bias=config.bias,
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skip_bias_add=True,
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quant_config=quant_config,
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reduce_results=self.reduce_row_parallel_results)
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self.use_rotary = config.rotary
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self.use_alibi = config.alibi
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assert not (self.use_rotary and self.use_alibi), (
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"Rotary and alibi are mutually exclusive.")
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if self.use_rotary:
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rope_theta = getattr(config, "rope_theta", 10000)
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max_position_embeddings = getattr(config,
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"max_position_embeddings", 8192)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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)
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.inv_norm_factor,
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num_kv_heads=self.num_kv_heads,
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quant_config=quant_config)
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elif self.use_alibi:
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tp_rank = get_tensor_model_parallel_rank()
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head_start = tp_rank * self.num_heads
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head_end = (tp_rank + 1) * self.num_heads
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alibi_slopes = (_get_alibi_slopes(self.total_num_heads) *
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self.inv_norm_factor)
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alibi_slopes = alibi_slopes[head_start:head_end].tolist()
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.inv_norm_factor,
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num_kv_heads=self.num_kv_heads,
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alibi_slopes=alibi_slopes,
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quant_config=quant_config)
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else:
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self.attn = Attention(self.num_heads,
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self.head_dim,
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scale=self.inv_norm_factor,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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qkv, bias = self.query_key_value(hidden_states)
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if bias is not None:
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qkv += bias
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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if self.use_rotary:
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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attn_output, bias = self.dense(attn_output)
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return attn_output, bias
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class FalconMLP(nn.Module):
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def __init__(
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self,
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config: FalconConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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hidden_size = config.hidden_size
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self.dense_h_to_4h = ColumnParallelLinear(hidden_size,
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4 * hidden_size,
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bias=config.bias,
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skip_bias_add=True,
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quant_config=quant_config)
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self.act = get_act_fn("gelu")
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self.reduce_row_parallel_results = not (config.new_decoder_architecture
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or config.parallel_attn)
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self.dense_4h_to_h = RowParallelLinear(
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4 * hidden_size,
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hidden_size,
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bias=config.bias,
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skip_bias_add=True,
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reduce_results=self.reduce_row_parallel_results,
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quant_config=quant_config)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# NOTE(zhuohan): Following huggingface, we do not fuse bias add here.
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x, bias = self.dense_h_to_4h(x)
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if bias is not None:
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x += bias
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x = self.act(x)
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x, bias = self.dense_4h_to_h(x)
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return x, bias
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class FalconDecoderLayer(nn.Module):
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def __init__(
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self,
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config: FalconConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.self_attention = FalconAttention(config, cache_config,
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quant_config)
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self.mlp = FalconMLP(config, quant_config)
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self.config = config
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if (config.num_ln_in_parallel_attn is None
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and config.new_decoder_architecture):
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config.num_ln_in_parallel_attn = 2
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if not config.parallel_attn:
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self.post_attention_layernorm = LayerNorm(
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hidden_size, eps=config.layer_norm_epsilon)
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self.input_layernorm = LayerNorm(hidden_size,
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eps=config.layer_norm_epsilon)
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else:
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if config.num_ln_in_parallel_attn == 2:
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# The layer norm before self-attention
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self.ln_attn = LayerNorm(hidden_size,
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eps=config.layer_norm_epsilon)
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# The layer norm before the MLP
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self.ln_mlp = LayerNorm(hidden_size,
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eps=config.layer_norm_epsilon)
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else:
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self.input_layernorm = LayerNorm(hidden_size,
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eps=config.layer_norm_epsilon)
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self.reduce_row_parallel_results = not (config.new_decoder_architecture
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or config.parallel_attn)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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residual = hidden_states
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if self.config.num_ln_in_parallel_attn == 2:
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attention_layernorm_out = self.ln_attn(hidden_states)
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mlp_layernorm_out = self.ln_mlp(hidden_states)
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else:
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attention_layernorm_out = self.input_layernorm(hidden_states)
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# Self attention.
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attention_output, attention_bias = self.self_attention(
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positions=positions,
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hidden_states=attention_layernorm_out,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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)
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if self.reduce_row_parallel_results and attention_bias is not None:
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attention_output += attention_bias
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if not self.config.new_decoder_architecture:
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if self.config.parallel_attn:
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mlp_layernorm_out = attention_layernorm_out
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else:
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residual += attention_output
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mlp_layernorm_out = self.post_attention_layernorm(residual)
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if (self.config.new_decoder_architecture and self.config.parallel_attn
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and self.config.num_ln_in_parallel_attn == 1):
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mlp_layernorm_out = attention_layernorm_out
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# MLP.
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mlp_output, mlp_bias = self.mlp(mlp_layernorm_out)
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if self.reduce_row_parallel_results and mlp_bias is not None:
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mlp_output += mlp_bias
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if not self.reduce_row_parallel_results:
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# When MLP and Attention layers are parallel, we can use
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# only one all-reduce operator to reduce the results from
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# both MLP and Attention layers.
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mlp_output += attention_output
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mlp_output = tensor_model_parallel_all_reduce(mlp_output)
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if attention_bias is not None:
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mlp_output += attention_bias
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if mlp_bias is not None:
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mlp_output += mlp_bias
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output = mlp_output + residual
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return output
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@support_torch_compile
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class FalconModel(nn.Module):
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def __init__(
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self,
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config: FalconConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.use_alibi = config.alibi
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# Embedding + LN Embedding
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self.word_embeddings = VocabParallelEmbedding(
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config.vocab_size,
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self.embed_dim,
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)
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# Transformer blocks
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self.start_layer, self.end_layer, self.h = make_layers(
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config.num_hidden_layers,
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lambda prefix: FalconDecoderLayer(config, cache_config,
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quant_config),
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prefix=f"{prefix}.h")
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# Final Layer Norm
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self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(["hidden_states"],
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config.hidden_size))
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors],
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) -> Union[torch.Tensor, IntermediateTensors]:
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if get_pp_group().is_first_rank:
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hidden_states = self.word_embeddings(input_ids)
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else:
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hidden_states = intermediate_tensors["hidden_states"]
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for i in range(self.start_layer, self.end_layer):
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layer = self.h[i]
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hidden_states = layer(
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positions,
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hidden_states,
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kv_caches[i - self.start_layer],
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attn_metadata,
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)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({"hidden_states": hidden_states})
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hidden_states = self.ln_f(hidden_states)
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return hidden_states
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class FalconForCausalLM(nn.Module, SupportsPP):
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# BitandBytes specific attributes
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bitsandbytes_stacked_params_mapping = {}
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default_bitsandbytes_target_modules = [
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".query_key_value.",
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".dense.",
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".dense_h_to_4h.",
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".dense_4h_to_h.",
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]
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def __init__(
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self,
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config: FalconConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.config = config
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self.quant_config = quant_config
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self.transformer = FalconModel(config, cache_config, quant_config)
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# only Falcon-11B doesn't share lm_head weight with word embeddings
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# and previous Falcon model doesn't have tie_word_embeddings config
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# so we set tie_word_embeddings to True by default
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self.tie_word_embeddings = (config.tie_word_embeddings
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if config.tie_word_embeddings is not None
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else True)
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if self.tie_word_embeddings:
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self.lm_head = self.transformer.word_embeddings
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else:
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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)
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.sampler = get_sampler()
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self.make_empty_intermediate_tensors = (
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self.transformer.make_empty_intermediate_tensors)
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def forward(
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self,
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input_ids: torch.LongTensor,
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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) -> torch.Tensor:
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hidden_states = self.transformer(input_ids, positions, kv_caches,
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attn_metadata, intermediate_tensors)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def sample(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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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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total_num_heads = self.config.num_attention_heads
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if self.config.new_decoder_architecture:
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total_num_kv_heads = self.config.num_kv_heads
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elif self.config.multi_query:
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total_num_kv_heads = 1
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else:
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total_num_kv_heads = total_num_heads
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num_query_heads_per_kv_head = total_num_heads // total_num_kv_heads
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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for name, loaded_weight in weights:
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|
if name == "lm_head.weight" and self.tie_word_embeddings:
|
|
# Falcon uses tied embeddings except Falcon-11b.
|
|
continue
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
param = params_dict[name]
|
|
if "query_key_value" in name:
|
|
output_dim = getattr(param, "output_dim", None)
|
|
loaded_weight_shape = loaded_weight.shape
|
|
if output_dim is not None:
|
|
loaded_weight = loaded_weight.view(
|
|
loaded_weight_shape[:output_dim] +
|
|
(total_num_kv_heads, num_query_heads_per_kv_head + 2,
|
|
-1) + loaded_weight_shape[output_dim + 1:])
|
|
wq = loaded_weight.narrow(
|
|
output_dim + 1, 0,
|
|
num_query_heads_per_kv_head).reshape(
|
|
*loaded_weight_shape[:output_dim], -1,
|
|
*loaded_weight_shape[output_dim + 1:])
|
|
wk = loaded_weight.narrow(
|
|
output_dim + 1, num_query_heads_per_kv_head,
|
|
1).reshape(*loaded_weight_shape[:output_dim], -1,
|
|
*loaded_weight_shape[output_dim + 1:])
|
|
wv = loaded_weight.narrow(
|
|
output_dim + 1, num_query_heads_per_kv_head + 1,
|
|
1).reshape(*loaded_weight_shape[:output_dim], -1,
|
|
*loaded_weight_shape[output_dim + 1:])
|
|
loaded_weight = torch.cat([wq, wk, wv], dim=output_dim)
|
|
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|