419 lines
20 KiB
Python
419 lines
20 KiB
Python
# Copyright 2024 Bytedance Ltd. and/or its affiliates
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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 math
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from typing import Optional, Tuple
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import torch
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from megatron.core import parallel_state as mpu
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from megatron.core import tensor_parallel
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from megatron.core import ModelParallelConfig
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from torch import nn
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from transformers import LlamaConfig
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from verl.models.llama.megatron.layers.parallel_linear import QKVParallelLinear
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from verl.utils.megatron import tensor_parallel as tp_utils
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class LlamaRotaryEmbedding(nn.Module):
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
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super().__init__()
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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inv_freq = 1.0 / (self.base**(torch.arange(0, self.dim, 2).float().to(device) / self.dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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# Build here to make `torch.jit.trace` work.
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self._set_cos_sin_cache(seq_len=max_position_embeddings,
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device=self.inv_freq.device,
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dtype=torch.get_default_dtype())
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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self.max_seq_len_cached = seq_len
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
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freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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def forward(self, x, seq_len=None):
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# x: [bs, num_attention_heads, seq_len, head_size]
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if seq_len > self.max_seq_len_cached:
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self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
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return (
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self.cos_cached[:seq_len].to(dtype=x.dtype),
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self.sin_cached[:seq_len].to(dtype=x.dtype),
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)
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class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
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"""LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
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self.scaling_factor = scaling_factor
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super().__init__(dim, max_position_embeddings, base, device)
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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self.max_seq_len_cached = seq_len
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
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t = t / self.scaling_factor
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freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
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"""LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
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self.scaling_factor = scaling_factor
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super().__init__(dim, max_position_embeddings, base, device)
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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self.max_seq_len_cached = seq_len
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if seq_len > self.max_position_embeddings:
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base = self.base * ((self.scaling_factor * seq_len / self.max_position_embeddings) -
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(self.scaling_factor - 1))**(self.dim / (self.dim - 2))
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inv_freq = 1.0 / (base**(torch.arange(0, self.dim, 2).float().to(device) / self.dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
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freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., :x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2:]
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
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cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
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sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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class ParallelLlamaAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig):
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super().__init__()
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self.config = config
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self.megatron_config = megatron_config
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.hidden_size // self.num_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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self.max_position_embeddings = config.max_position_embeddings
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self.rope_theta = config.rope_theta
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# assign values after tp
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tp_size = mpu.get_tensor_model_parallel_world_size()
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assert self.num_heads % tp_size == 0, f'num_head must be divisible by tp_size. Got num_head={self.num_heads}, tp_size={tp_size}'
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assert self.num_key_value_heads % tp_size == 0, \
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f'num_key_value_heads must be divisible by tp_size. Got num_key_value_heads={self.num_key_value_heads}, tp_size={tp_size}'
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self.num_heads_per_tp = self.num_heads // tp_size
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self.num_key_value_heads_per_tp = self.num_key_value_heads // tp_size
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self.hidden_size_per_tp = self.hidden_size // tp_size
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if (self.head_dim * self.num_heads) != self.hidden_size:
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raise ValueError(f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
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f" and `num_heads`: {self.num_heads}).")
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column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear()
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row_kwargs = tp_utils.get_default_kwargs_for_row_parallel_linear()
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if megatron_config is not None:
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assert column_kwargs.get('config', False), 'must have ModelParallelConfig'
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assert row_kwargs.get('config', False), 'must have ModelParallelConfig'
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tp_utils.update_kwargs_with_config(column_kwargs, megatron_config)
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tp_utils.update_kwargs_with_config(row_kwargs, megatron_config)
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# [self.q_size, self.k_size, self.v_size]
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self.qkv_proj = QKVParallelLinear(input_size=self.hidden_size,
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num_heads=self.num_heads,
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num_key_value_heads=self.num_key_value_heads,
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head_dim=self.head_dim,
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bias=config.attention_bias,
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gather_output=False,
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skip_bias_add=False,
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**column_kwargs)
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self.q_size = self.num_heads_per_tp * self.head_dim
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self.k_size = self.num_key_value_heads_per_tp * self.head_dim
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self.v_size = self.num_key_value_heads_per_tp * self.head_dim
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self.o_proj = tensor_parallel.RowParallelLinear(input_size=self.num_heads * self.head_dim,
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output_size=self.hidden_size,
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bias=config.attention_bias,
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input_is_parallel=True,
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skip_bias_add=False,
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**row_kwargs)
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self._init_rope()
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def _init_rope(self):
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if self.config.rope_scaling is None:
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self.rotary_emb = LlamaRotaryEmbedding(
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self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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base=self.rope_theta,
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)
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else:
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scaling_type = self.config.rope_scaling["type"]
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scaling_factor = self.config.rope_scaling["factor"]
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if scaling_type == "linear":
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self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
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self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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scaling_factor=scaling_factor,
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base=self.rope_theta,
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)
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elif scaling_type == "dynamic":
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self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
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self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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scaling_factor=scaling_factor,
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base=self.rope_theta,
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)
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else:
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raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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qkv = self.qkv_proj(hidden_states)[0]
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query_states, key_states, value_states = qkv.split([self.q_size, self.k_size, self.v_size], dim=-1)
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query_states = query_states.view(bsz, q_len, self.num_heads_per_tp, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads_per_tp, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads_per_tp, self.head_dim).transpose(1, 2)
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kv_seq_len = key_states.shape[-2]
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attn_weights.size() != (bsz, self.num_heads_per_tp, q_len, kv_seq_len):
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raise ValueError(
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f"Attention weights should be of size {(bsz, self.num_heads_per_tp, q_len, kv_seq_len)}, but is"
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f" {attn_weights.size()}")
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if attention_mask is not None:
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if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
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raise ValueError(
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}")
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attn_weights = attn_weights + attention_mask
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_output = torch.matmul(attn_weights, value_states)
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if attn_output.size() != (bsz, self.num_heads_per_tp, q_len, self.head_dim):
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raise ValueError(
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f"`attn_output` should be of size {(bsz, self.num_heads_per_tp, q_len, self.head_dim)}, but is"
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f" {attn_output.size()}")
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size_per_tp)
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attn_output = self.o_proj(attn_output)[0]
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return attn_output
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"""
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Remove padding Attention
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- Using Flash-attn 2
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- Compatible with sequence parallel
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"""
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from transformers.utils import is_flash_attn_2_available
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import torch.nn.functional as F
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from einops import rearrange
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if is_flash_attn_2_available():
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from flash_attn import flash_attn_varlen_func
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from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
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def apply_rotary_pos_emb_rmpad(q, k, cos, sin, position_ids, indices, sequence_length):
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batch_size = position_ids.shape[0]
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q = pad_input(q, indices, batch_size, sequence_length) # (batch_size, seqlen, num_head, head_dim)
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k = pad_input(k, indices, batch_size, sequence_length)
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cos = cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]
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sin = sin[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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q_embed = index_first_axis(rearrange(q_embed, "b s ... -> (b s) ..."), indices)
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k_embed = index_first_axis(rearrange(k_embed, "b s ... -> (b s) ..."), indices)
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return q_embed, k_embed
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from flash_attn.layers.rotary import apply_rotary_emb
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# use flash-attn rotary embeddings with rmpad
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# cos/sin shoudl be: (seq_length, rotary_dim / 2)
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def apply_rotary_pos_emb_rmpad_flash(q, k, cos, sin, cu_seqlens, max_seqlen):
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q_embed = apply_rotary_emb(q,
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cos,
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sin,
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interleaved=False,
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inplace=False,
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cu_seqlens=cu_seqlens,
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max_seqlen=max_seqlen)
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k_embed = apply_rotary_emb(k,
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cos,
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sin,
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interleaved=False,
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inplace=False,
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cu_seqlens=cu_seqlens,
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max_seqlen=max_seqlen)
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return q_embed, k_embed
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class ParallelLlamaAttentionRmPad(ParallelLlamaAttention):
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def forward(self,
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hidden_states: torch.Tensor,
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position_ids: Optional[torch.LongTensor] = None,
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sequence_length: int = None,
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indices: torch.Tensor = None,
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cu_seqlens: torch.Tensor = None,
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max_seqlen_in_batch: int = None):
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total_nnz, _, _ = hidden_states.size() # This is the total_nnz padded after sequence parallel
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if self.megatron_config.sequence_parallel:
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total_nnz = total_nnz * mpu.get_tensor_model_parallel_world_size()
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qkv = self.qkv_proj(hidden_states)[0]
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query_states, key_states, value_states = qkv.split([self.q_size, self.k_size, self.v_size],
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dim=-1) # (total_nnz, 1, hidden_size)
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if self.megatron_config.sequence_parallel:
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sequence_parallel_pad = total_nnz - cu_seqlens[-1]
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total_nnz = cu_seqlens[-1] # total_nnz before sp padding
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query_states = query_states[:total_nnz]
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key_states = key_states[:total_nnz]
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value_states = value_states[:total_nnz]
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# Flash attention requires the input to have the shape
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# batch_size x seq_length x head_dime x hidden_dim
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# therefore we just need to keep the original shape
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query_states = query_states.view(total_nnz, self.num_heads_per_tp, self.head_dim)
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key_states = key_states.view(total_nnz, self.num_key_value_heads_per_tp, self.head_dim)
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value_states = value_states.view(total_nnz, self.num_key_value_heads_per_tp, self.head_dim)
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cos, sin = self.rotary_emb(value_states, seq_len=sequence_length)
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cos, sin = cos[:, :cos.shape[1] // 2], sin[:, :sin.shape[1] // 2] # flash attn only needs half
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query_states, key_states = apply_rotary_pos_emb_rmpad_flash(query_states,
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key_states,
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cos,
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sin,
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cu_seqlens=cu_seqlens,
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max_seqlen=max_seqlen_in_batch)
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# query_states, key_states = apply_rotary_pos_emb_rmpad(query_states, key_states, cos, sin, position_ids, indices,
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# TODO: llama does not have dropout in the config??
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# It is recommended to use dropout with FA according to the docs
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# when training.
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dropout_rate = 0.0 # if not self.training else self.attn_dropout
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# In PEFT, usually we cast the layer norms in float32 for training stability reasons
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# therefore the input hidden states gets silently casted in float32. Hence, we need
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# cast them back in float16 just to be sure everything works as expected.
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# This might slowdown training & inference so it is recommended to not cast the LayerNorms
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# in fp32. (LlamaRMSNorm handles it correctly)
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input_dtype = query_states.dtype
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if input_dtype == torch.float32:
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query_states = query_states.to(torch.float16)
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key_states = key_states.to(torch.float16)
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|
value_states = value_states.to(torch.float16)
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|
|
|
attn_output_unpad = flash_attn_varlen_func(
|
|
query_states,
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|
key_states,
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|
value_states,
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|
cu_seqlens_q=cu_seqlens,
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|
cu_seqlens_k=cu_seqlens,
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|
max_seqlen_q=max_seqlen_in_batch,
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|
max_seqlen_k=max_seqlen_in_batch,
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|
dropout_p=dropout_rate,
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|
softmax_scale=None,
|
|
causal=True,
|
|
)
|
|
|
|
attn_output_unpad = attn_output_unpad.to(input_dtype)
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|
attn_output_unpad = attn_output_unpad.reshape(total_nnz, 1, self.hidden_size_per_tp).contiguous()
|
|
|
|
# sequence parallel reduce_scatter is performed inside RowColumnParallel if enabled
|
|
# Here we need to repad
|
|
if self.megatron_config.sequence_parallel:
|
|
attn_output_unpad = F.pad(attn_output_unpad, pad=(0, 0, 0, 0, 0, sequence_parallel_pad))
|
|
|
|
attn_output_unpad = self.o_proj(attn_output_unpad)[0]
|
|
return attn_output_unpad
|