469 lines
14 KiB
Python
469 lines
14 KiB
Python
# coding=utf-8
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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import torch
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import torch.distributed
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from torch import nn
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from transformers.activations import ACT2FN
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from typing import Optional
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# Flash attention imports
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import flash_attn_cuda
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import dropout_layer_norm
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from text_generation_server.utils.layers import (
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FastLinear,
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TensorParallelRowLinear,
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TensorParallelColumnLinear,
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TensorParallelEmbedding,
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PositionRotaryEmbedding,
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)
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class LlamaRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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LlamaRMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states, residual=None):
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if hidden_states.shape[-1] > 8192:
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if residual is not None:
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hidden_states += residual
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residual = hidden_states
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variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(
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variance + self.variance_epsilon
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)
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# convert into half-precision if necessary
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if self.weight.dtype in [torch.float16, torch.bfloat16]:
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hidden_states = hidden_states.to(self.weight.dtype)
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return self.weight * hidden_states, residual
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else:
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# faster post attention rms norm
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normed_hidden_states, res, *rest = dropout_layer_norm.dropout_add_ln_fwd(
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hidden_states,
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residual,
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self.weight,
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None,
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None,
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None,
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None,
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None,
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0.0,
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self.variance_epsilon,
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1.0,
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0,
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None,
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False,
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True, # Activate RMSNorm
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)
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if res is None:
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res = hidden_states
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return normed_hidden_states, res
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class FlashLlamaAttention(torch.nn.Module):
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def __init__(
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self,
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num_heads,
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hidden_size,
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process_group=None,
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):
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super().__init__()
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self.num_heads = num_heads
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self.hidden_size = hidden_size
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self.head_size = hidden_size // num_heads
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self.rotary_emb = PositionRotaryEmbedding(self.head_size, base=10000)
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self.softmax_scale = self.head_size ** (-0.5)
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if process_group is None:
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self.query_key_value = FastLinear(hidden_size, 3 * hidden_size, bias=False)
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self.o_proj = FastLinear(hidden_size, hidden_size, bias=False)
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else:
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self.num_heads = self.num_heads // process_group.size()
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self.query_key_value = TensorParallelColumnLinear(
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hidden_size,
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3 * hidden_size,
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bias=False,
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process_group=process_group,
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)
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self.o_proj = TensorParallelRowLinear(
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hidden_size,
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hidden_size,
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bias=False,
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process_group=process_group,
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)
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def forward(
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self,
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hidden_states,
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cos,
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sin,
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cu_seqlens,
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max_s,
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layer_past,
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layer_past_present_indices,
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cu_seqlens_q,
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):
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qkv = self.query_key_value(hidden_states)
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qkv = qkv.view(-1, 3, self.num_heads, self.head_size)
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# Inplace rotary
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self.rotary_emb(qkv[:, 0], cos, sin)
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self.rotary_emb(qkv[:, 1], cos, sin)
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# Prefill
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if layer_past_present_indices is None:
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# Copy to layer past
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layer_past[...] = qkv[:, 1:]
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# output
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attn_output = torch.empty_like(qkv[:, 0])
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# flash attention
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flash_attn_cuda.fwd(
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qkv[:, 0],
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qkv[:, 1],
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qkv[:, 2],
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attn_output,
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cu_seqlens,
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cu_seqlens,
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max_s,
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max_s,
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0.0,
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self.softmax_scale,
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False,
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True,
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False,
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0,
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None,
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)
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# Decode
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else:
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query = qkv[:, 0]
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# Add present to the layer_past tensor at the correct indices
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layer_past[layer_past_present_indices] = qkv[:, 1:]
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# output
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attn_output = torch.empty_like(query)
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# flash attention
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flash_attn_cuda.fwd(
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query,
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layer_past[:, 0],
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layer_past[:, 1],
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attn_output,
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cu_seqlens_q,
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cu_seqlens,
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1,
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max_s,
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0.0,
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self.softmax_scale,
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False,
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False,
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False,
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0,
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None,
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)
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return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
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class LlamaMLP(nn.Module):
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def __init__(self, act, hidden_size, intermediate_size, process_group=None):
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super().__init__()
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self.act = (
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ACT2FN[act]
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if "gelu" not in act
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else lambda x: torch.nn.functional.gelu(
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x,
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approximate="tanh"
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if act in ["gelu_fast", "gelu_pytorch_tanh"]
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else "none",
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)
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)
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if process_group is None:
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# Fuse gate and up proj
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self.gate_up_proj = FastLinear(
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hidden_size, 2 * intermediate_size, bias=False
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)
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self.down_proj = FastLinear(intermediate_size, hidden_size, bias=False)
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self.intermediate_size = intermediate_size
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else:
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# Fuse gate and up proj
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self.gate_up_proj = TensorParallelColumnLinear(
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hidden_size,
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2 * intermediate_size,
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bias=False,
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process_group=process_group,
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)
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self.down_proj = TensorParallelRowLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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process_group=process_group,
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reduce=True,
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)
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self.intermediate_size = self.down_proj.in_features
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self.process_group = process_group
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def forward(self, hidden_states):
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gate_up_states = self.gate_up_proj(hidden_states)
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gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
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return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
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class FlashLlamaLayer(nn.Module):
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def __init__(
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self,
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num_heads,
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act,
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hidden_size,
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intermediate_size,
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rms_norm_eps,
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process_group=None,
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):
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super().__init__()
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self.self_attn = FlashLlamaAttention(num_heads, hidden_size, process_group)
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self.mlp = LlamaMLP(act, hidden_size, intermediate_size, process_group)
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self.input_layernorm = LlamaRMSNorm(hidden_size, eps=rms_norm_eps)
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self.post_attention_layernorm = LlamaRMSNorm(hidden_size, eps=rms_norm_eps)
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def forward(
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self,
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hidden_states,
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residual,
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cos,
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sin,
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cu_seqlens,
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max_s,
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layer_past,
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layer_past_present_indices,
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cu_seqlens_q,
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):
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normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
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# Self Attention
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attn_output = self.self_attn(
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normed_hidden_states,
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cos,
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sin,
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cu_seqlens,
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max_s,
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layer_past,
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layer_past_present_indices,
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cu_seqlens_q,
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)
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# faster post attention rms norm
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normed_attn_res_output, attn_res = self.post_attention_layernorm(
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attn_output, res
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)
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mlp_output = self.mlp(normed_attn_res_output)
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return mlp_output, attn_res
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class FlashLlamaModel(torch.nn.Module):
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def __init__(self, config, process_group=None):
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super(FlashLlamaModel, self).__init__()
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self.config = config
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self.tp_embeddings = False
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if process_group is not None:
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self.tp_rank = process_group.rank()
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self.tp_world_size = process_group.size()
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if config.vocab_size % self.tp_world_size == 0:
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self.tp_embeddings = True
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if self.tp_embeddings:
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self.embed_tokens = TensorParallelEmbedding(
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config.vocab_size, config.hidden_size, process_group=process_group
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)
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else:
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self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
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self.layers = nn.ModuleList(
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[
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FlashLlamaLayer(
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config.num_attention_heads,
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config.hidden_act,
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config.hidden_size,
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config.intermediate_size,
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config.rms_norm_eps,
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process_group,
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)
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for _ in range(config.num_hidden_layers)
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]
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)
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self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.gradient_checkpointing = False
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self.head_size = self.layers[0].self_attn.head_size
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self.num_heads = self.layers[0].self_attn.num_heads
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def post_load_weights(self, quantize: Optional[str] = None):
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if isinstance(self.embed_tokens, TensorParallelEmbedding):
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self.embed_tokens.add_null_idx()
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for layer in self.layers:
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layer: FlashLlamaLayer
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layer.self_attn.query_key_value.prepare_weights(quantize)
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layer.self_attn.o_proj.prepare_weights(quantize)
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layer.mlp.gate_up_proj.prepare_weights(quantize)
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layer.mlp.down_proj.prepare_weights(quantize)
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def forward(
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self,
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input_ids,
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position_ids,
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cu_seqlens,
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cu_seqlens_q,
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max_s,
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past_key_values: Optional[torch.Tensor] = None,
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pre_allocate_past_size: Optional[int] = None,
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):
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hidden_states = self.embed_tokens(input_ids)
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# Prefill
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if past_key_values is None:
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# Create past tensor
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past_key_values = hidden_states.new_empty(
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(
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len(self.layers),
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len(hidden_states)
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if pre_allocate_past_size is None
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else pre_allocate_past_size,
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2,
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self.num_heads,
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self.head_size,
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)
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)
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layer_past_present_indices = None
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slice_past_index = len(hidden_states)
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# Decode
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else:
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# Create indices from cumulative sequence lengths
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layer_past_present_indices = cu_seqlens[1:] - 1
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slice_past_index = None
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# Get rotary cos and sin for this forward
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# Avoid to index in each layer
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cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
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position_ids, max_s, hidden_states.dtype
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)
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residual = None
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for i, layer in enumerate(self.layers):
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# We added padding that we now need to slice
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layer_past_key_values = (
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past_key_values[i]
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if slice_past_index is None
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else past_key_values[i, :slice_past_index]
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)
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hidden_states, residual = layer(
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hidden_states,
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residual,
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cos,
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sin,
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cu_seqlens,
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max_s,
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layer_past_key_values,
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layer_past_present_indices,
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cu_seqlens_q,
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)
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states, past_key_values
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class FlashLlamaForCausalLM(torch.nn.Module):
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def __init__(self, config, process_group=None):
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super().__init__()
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self.process_group = process_group
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if self.process_group is not None:
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self.world_size = self.process_group.size()
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else:
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self.world_size = 1
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self.model = FlashLlamaModel(config, process_group)
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if self.model.tp_embeddings:
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self.lm_head = FastLinear(
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config.hidden_size,
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config.vocab_size // process_group.size(),
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bias=False,
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)
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else:
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self.lm_head = FastLinear(config.hidden_size, config.vocab_size, bias=False)
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def post_load_weights(self, quantize: Optional[str] = None):
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self.model.post_load_weights(quantize)
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self.lm_head.prepare_weights()
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def forward(
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self,
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input_ids,
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position_ids,
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cu_seqlens,
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cu_seqlens_q,
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max_s,
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past_key_values: Optional[torch.Tensor] = None,
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pre_allocate_past_size: Optional[int] = None,
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lm_head_indices: Optional[torch.Tensor] = None,
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):
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hidden_states, present = self.model(
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input_ids,
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position_ids,
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cu_seqlens,
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cu_seqlens_q,
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max_s,
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past_key_values,
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pre_allocate_past_size,
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)
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if lm_head_indices is not None:
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hidden_states = hidden_states[lm_head_indices]
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logits = self.lm_head(hidden_states)
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if self.model.tp_embeddings:
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# Logits are sharded, so we need to gather them
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world_logits = [torch.empty_like(logits) for _ in range(self.world_size)]
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torch.distributed.all_gather(world_logits, logits, group=self.process_group)
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world_logits = torch.cat(world_logits, dim=1)
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return world_logits, present
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return logits, present
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