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"""PyTorch TELECHAT model.""" |
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|
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from typing import Optional, Tuple, Union |
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|
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import math |
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import torch |
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from einops import rearrange |
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from torch import einsum, nn |
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from torch.cuda.amp import autocast |
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import torch.nn.functional as F |
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from transformers.activations import ACT2FN |
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from transformers.modeling_outputs import ( |
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BaseModelOutputWithPastAndCrossAttentions, |
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CausalLMOutputWithCrossAttentions, |
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SequenceClassifierOutputWithPast, |
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) |
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from transformers.modeling_utils import PreTrainedModel |
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from transformers.pytorch_utils import find_pruneable_heads_and_indices, prune_conv1d_layer |
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from transformers.utils import logging |
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from transformers.utils.model_parallel_utils import assert_device_map, get_device_map |
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try: |
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from flash_attn.flash_attn_interface import flash_attn_unpadded_func |
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print("# FLASH ATTENTION 1 DETECTED #") |
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except ImportError: |
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try: |
|
from flash_attn.flash_attn_interface import flash_attn_varlen_func as flash_attn_unpadded_func |
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print("# FLASH ATTENTION 2 DETECTED #") |
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except ImportError: |
|
print("# NO FLASH ATTENTION DETECTED #") |
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flash_attn_unpadded_func = None |
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from .configuration_telechat import TELECHATConfig |
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|
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def debug_print_tensor(t, name, title='', show_dim=10): |
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|
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prefix = f'{title} -> ' |
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if isinstance(t, torch.Tensor): |
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if len(t.shape) == 1: |
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output = f"{name}[{t.shape}]: {t[:show_dim]}" |
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elif len(t.shape) == 2: |
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output = f"{name}[{t.shape}]: {t[-1, :show_dim]}" |
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elif len(t.shape) == 3: |
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output = f" {name}[{t.shape}]: {t[-1, -1, :show_dim]}" |
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elif len(t.shape) == 4: |
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output = f"{name}[{t.shape}]: {t[-1, -1, -1, :show_dim]}" |
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else: |
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output = f"{name}[{t.shape}]" |
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elif isinstance(t, list): |
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output = f"{name} [{len(t)}]: {t[:show_dim]}" |
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else: |
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output = f"{name} 未知类型: {type(t)}" |
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print(prefix + output) |
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|
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class Conv1D(nn.Module): |
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|
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def __init__(self, nf, nx, bias=True): |
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super().__init__() |
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self.nf = nf |
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self.weight = nn.Parameter(torch.empty(nx, nf)) |
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self.bias = None |
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if bias: |
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self.bias = nn.Parameter(torch.zeros(nf)) |
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nn.init.normal_(self.weight, std=0.02) |
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|
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def forward(self, x): |
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if self.bias is not None: |
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return torch.matmul(x, self.weight) + self.bias |
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else: |
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return torch.matmul(x, self.weight) |
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|
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class RMSNorm(nn.Module): |
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def __init__(self, hidden_size, eps=1e-5): |
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super().__init__() |
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self.weight = nn.Parameter(torch.ones(hidden_size)) |
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self.eps = eps |
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|
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def forward(self, hidden_states): |
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input_dtype = hidden_states.dtype |
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hidden_states = hidden_states.to(torch.float32) |
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variance = hidden_states.pow(2).mean(-1, keepdim=True) |
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hidden_states = hidden_states * torch.rsqrt(variance + self.eps) |
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return self.weight * hidden_states.to(input_dtype) |
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logger = logging.get_logger(__name__) |
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|
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def exists(v): |
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return v is not None |
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|
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class RotaryEmbedding(nn.Module): |
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def __init__(self, dim, use_xpos=False, xpos_scale_base=512, theta=10000): |
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super().__init__() |
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inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim)) |
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self.register_buffer('inv_freq', inv_freq) |
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self.cache = dict() |
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self.cache_scale = dict() |
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self.use_xpos = use_xpos |
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if not use_xpos: |
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self.register_buffer('scale', None) |
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return |
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scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim) |
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self.register_buffer('scale', scale) |
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self.scale_base = xpos_scale_base |
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|
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def forward(self, seq, cache_key=None): |
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|
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if cache_key is not None and cache_key in self.cache: |
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return self.cache[cache_key] |
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inv_freq = self.inv_freq.to(device=seq.device) |
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freqs = einsum('i , j -> i j', seq, inv_freq) |
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scale = torch.cat((freqs, freqs), dim=-1) |
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if exists(cache_key): |
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self.cache[cache_key] = scale |
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return scale |
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|
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def rotate_queries_and_keys(self, q, k, seq_dim=-2): |
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""" |
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use this only when xpos is activated. |
|
""" |
|
assert self.use_xpos and q.device == k.device |
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device, seq_len_k, seq_len_q = k.device, k.shape[seq_dim], q.shape[seq_dim] |
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pos_seq_k = torch.arange(seq_len_k, device=device, dtype=torch.float32) |
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pos_seq_q = torch.arange(seq_len_k - seq_len_q, seq_len_k, device=device, dtype=torch.float32) |
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freqs_k = self.forward(pos_seq_k, cache_key=f"{0}:{seq_len_k}") |
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freqs_q = self.forward(pos_seq_q, cache_key=f"{seq_len_k - seq_len_q}:{seq_len_k}") |
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scale_k = self.get_scale(pos_seq_k) |
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scale_q = self.get_scale(pos_seq_q, offset=seq_len_k - seq_len_q) |
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rotated_q = apply_rotary_emb(freqs_q, q, scale=scale_q) |
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rotated_k = apply_rotary_emb(freqs_k, k, scale=scale_k ** -1) |
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return rotated_q, rotated_k |
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|
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def rotate_queries_or_keys(self, t, seq_dim=-2, offset=0): |
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""" |
|
use this only when xpos is NOT activated. |
|
""" |
|
|
|
assert not self.use_xpos, 'you must use `.rotate_queries_and_keys` method instead and pass in both queries and keys, for length extrapolatable rotary embeddings' |
|
device, seq_len = t.device, t.shape[seq_dim] |
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pos_seq_t = torch.arange(offset, offset + seq_len, device=device, dtype=torch.float32) |
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freqs = self.forward(pos_seq_t, cache_key=f"{offset}:{offset+seq_len}") |
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return apply_rotary_emb(freqs, t) |
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|
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def get_scale(self, t, cache_key=None, offset=0, ): |
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assert self.use_xpos, 'This function is only useful for xpos.' |
|
if exists(cache_key) and cache_key in self.cache_scale: |
|
return self.cache_scale[cache_key] |
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if callable(t): |
|
t = t() |
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length = len(t) |
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min_pos = -(length + offset) // 2 |
|
max_pos = length + offset + min_pos |
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power = torch.arange(min_pos, max_pos, 1).to(device=self.scale.device) / self.scale_base |
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scale = self.scale ** rearrange(power, 'n -> n 1') |
|
scale = scale[-length:, :] |
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scale = torch.cat((scale, scale), dim=-1) |
|
if exists(cache_key): |
|
self.cache_scale[cache_key] = scale |
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return scale |
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|
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|
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def rotate_half(x): |
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""" |
|
change sign so the last dimension becomes [-odd, +even] |
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""" |
|
x1, x2 = torch.chunk(x, 2, dim=-1) |
|
return torch.cat((-x2, x1), dim=-1) |
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|
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|
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def apply_rotary_emb(freqs, t, start_index=0, scale=1.): |
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""" |
|
freq: seqlen x dim |
|
t: [batchsize * headnum , seqlen , dim (dim_of_head actually)] |
|
""" |
|
dtype_t = t.dtype |
|
freqs = freqs.to(device=t.device) |
|
if isinstance(scale, torch.Tensor): |
|
scale = scale.to(device=t.device) |
|
rot_dim = freqs.shape[-1] |
|
end_index = start_index + rot_dim |
|
t_left, t, t_right = t[..., :start_index], t[..., start_index:end_index], t[..., end_index:] |
|
t = (t * freqs.cos() + rotate_half(t) * freqs.sin()) * scale |
|
rotated = torch.cat((t_left, t, t_right), dim=-1) |
|
rotated = rotated.to(dtype=dtype_t) |
|
return rotated |
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|
|
|
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class TELECHATAttention(nn.Module): |
|
def __init__(self, config, layer_idx=None): |
|
super().__init__() |
|
|
|
max_positions = config.max_position_embeddings |
|
self.register_buffer( |
|
"bias", |
|
torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view( |
|
1, 1, max_positions, max_positions |
|
), |
|
) |
|
self.register_buffer("masked_bias", torch.tensor(-1e4)) |
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|
|
self.embed_dim = config.hidden_size |
|
self.num_heads = config.num_attention_heads |
|
self.head_dim = self.embed_dim // self.num_heads |
|
self.split_size = self.embed_dim |
|
if self.head_dim * self.num_heads != self.embed_dim: |
|
raise ValueError( |
|
f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" |
|
f" {self.num_heads})." |
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) |
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|
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self.scale_attn_weights = config.scale_attn_weights |
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self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx |
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|
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self.layer_idx = max(1, layer_idx) |
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self.reorder_and_upcast_attn = config.reorder_and_upcast_attn |
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|
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self.relative_encoding = config.relative_encoding |
|
self.rotary_use_xpos = config.rotary_use_xpos |
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|
|
self.use_mup = config.use_mup |
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|
|
self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim, bias=config.add_bias_linear) |
|
self.c_proj = Conv1D(self.embed_dim, self.embed_dim, bias=config.add_bias_linear) |
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|
|
self.attn_dropout = nn.Dropout(config.attn_pdrop) |
|
self.resid_dropout = nn.Dropout(config.resid_pdrop) |
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|
|
self.pruned_heads = set() |
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|
|
self.use_flash_attn = False |
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|
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|
|
|
def set_max_positions(self, max_positions, device='cuda'): |
|
self.max_positions = max_positions |
|
self.register_buffer( |
|
"bias", |
|
torch.tril(torch.ones((self.max_positions, self.max_positions), dtype=torch.bool)).view( |
|
1, 1, self.max_positions, self.max_positions |
|
).to(device=device) |
|
) |
|
|
|
def prune_heads(self, heads): |
|
if len(heads) == 0: |
|
return |
|
heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads) |
|
index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)]) |
|
|
|
|
|
self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1) |
|
self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0) |
|
|
|
|
|
self.split_size = (self.split_size // self.num_heads) * (self.num_heads - len(heads)) |
|
self.num_heads = self.num_heads - len(heads) |
|
self.pruned_heads = self.pruned_heads.union(heads) |
|
|
|
def _attn(self, query, key, value, attention_mask=None, head_mask=None): |
|
|
|
|
|
batch_size, head_num, k_seq_len, head_features = key.shape |
|
_, _, q_seq_len, _ = query.shape |
|
|
|
if self.use_flash_attn: |
|
|
|
|
|
|
|
|
|
|
|
batch_size, seqlen_q = query.shape[0], query.shape[2] |
|
seqlen_k = key.shape[2] |
|
|
|
query, key, value = [rearrange(x, 'b h s ... -> (b s) h ...') for x in [query, key, value]] |
|
cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32, |
|
device=query.device) |
|
is_causal = seqlen_q == seqlen_k |
|
cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32, |
|
device=query.device) |
|
dropout_p = 0 |
|
|
|
softmax_scale = 1/torch.full([], (value.size(-1) ** 0.5), dtype=value.dtype, device=value.device) if self.scale_attn_weights else 1 |
|
attn_output = flash_attn_unpadded_func( |
|
query, key, value, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k, |
|
dropout_p, |
|
softmax_scale=softmax_scale, causal=is_causal |
|
) |
|
attn_output = rearrange(attn_output, '(b s) h ... -> b h s ...', b=batch_size) |
|
attn_weights = None |
|
return attn_output, attn_weights |
|
|
|
attn_weights = torch.matmul(query, key.transpose(-1, -2)) |
|
|
|
if self.scale_attn_weights: |
|
if self.use_mup: |
|
attn_weights = attn_weights / torch.full( |
|
[], value.size(-1) / (value.size(-1) ** 0.5), dtype=attn_weights.dtype, |
|
device=attn_weights.device |
|
) |
|
else: |
|
attn_weights = attn_weights / torch.full( |
|
[], value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device |
|
) |
|
|
|
if not self.is_cross_attention: |
|
|
|
query_length, key_length = query.size(-2), key.size(-2) |
|
causal_mask = self.bias[:, :, key_length - query_length: key_length, :key_length] |
|
mask_value = torch.finfo(attn_weights.dtype).min |
|
|
|
|
|
mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to(attn_weights.device) |
|
attn_weights = torch.where(causal_mask, attn_weights.to(attn_weights.dtype), mask_value) |
|
|
|
if attention_mask is not None: |
|
|
|
attn_weights = attn_weights + attention_mask |
|
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1) |
|
|
|
|
|
attn_weights = attn_weights.type(value.dtype) |
|
attn_weights = self.attn_dropout(attn_weights) |
|
|
|
|
|
if head_mask is not None: |
|
attn_weights = attn_weights * head_mask |
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|
|
attn_output = torch.matmul(attn_weights, value) |
|
|
|
return attn_output, attn_weights |
|
|
|
def _upcast_and_reordered_attn(self, query, key, value, attention_mask=None, head_mask=None): |
|
|
|
bsz, num_heads, q_seq_len, dk = query.size() |
|
_, _, k_seq_len, _ = key.size() |
|
|
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|
|
attn_weights = torch.empty(bsz * num_heads, q_seq_len, k_seq_len, dtype=query.dtype, device=query.device) |
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|
|
|
|
scale_factor = 1.0 |
|
if self.scale_attn_weights: |
|
scale_factor /= float(value.size(-1)) ** 0.5 |
|
|
|
if self.scale_attn_by_inverse_layer_idx: |
|
scale_factor /= float(self.layer_idx) |
|
|
|
with autocast(enabled=False): |
|
q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(-1, dk, k_seq_len) |
|
attn_weights = torch.baddbmm(attn_weights, q, k, beta=0, alpha=scale_factor) |
|
attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len) |
|
|
|
if not self.is_cross_attention: |
|
attn_weights = attn_weights.float() |
|
if self.scale_attn_by_inverse_layer_idx: |
|
attn_weights *= self.layer_idx |
|
|
|
query_length, key_length = query.size(-2), key.size(-2) |
|
causal_mask = self.bias[:, :, key_length - query_length: key_length, :key_length] |
|
mask_value = -10000.0 |
|
|
|
|
|
mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device) |
|
attn_weights = torch.where(causal_mask, attn_weights, mask_value) |
|
|
|
if attention_mask is not None: |
|
|
|
attn_weights = attn_weights + attention_mask |
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1) |
|
|
|
if attn_weights.dtype != torch.float32: |
|
raise RuntimeError("Error with upcasting, attn_weights does not have dtype torch.float32") |
|
attn_weights = attn_weights.type(value.dtype) |
|
attn_weights = self.attn_dropout(attn_weights) |
|
|
|
|
|
if head_mask is not None: |
|
attn_weights = attn_weights * head_mask |
|
attn_output = torch.matmul(attn_weights, value) |
|
return attn_output, attn_weights |
|
|
|
def _split_heads(self, tensor, num_heads, attn_head_size): |
|
""" |
|
Splits hidden_size dim into attn_head_size and num_heads |
|
""" |
|
new_shape = tensor.size()[:-1] + (num_heads, attn_head_size) |
|
tensor = tensor.view(new_shape) |
|
return tensor.permute(0, 2, 1, 3) |
|
|
|
def _merge_heads(self, tensor, num_heads, attn_head_size): |
|
""" |
|
Merges attn_head_size dim and num_attn_heads dim into hidden_size |
|
""" |
|
tensor = tensor.permute(0, 2, 1, 3).contiguous() |
|
new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,) |
|
return tensor.view(new_shape) |
|
|
|
def forward( |
|
self, |
|
hidden_states: Optional[Tuple[torch.FloatTensor]], |
|
layer_past: Optional[Tuple[torch.Tensor]] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
encoder_hidden_states: Optional[torch.Tensor] = None, |
|
encoder_attention_mask: Optional[torch.FloatTensor] = None, |
|
rotary_embedding: Optional[RotaryEmbedding] = None, |
|
use_cache: Optional[bool] = False, |
|
output_attentions: Optional[bool] = False, |
|
) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]: |
|
if encoder_hidden_states is not None: |
|
if not hasattr(self, "q_attn"): |
|
raise ValueError( |
|
"If class is used as cross attention, the weights `q_attn` have to be defined. " |
|
"Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`." |
|
) |
|
|
|
query = self.q_attn(hidden_states) |
|
key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2) |
|
attention_mask = encoder_attention_mask |
|
else: |
|
query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2) |
|
|
|
query = self._split_heads(query, self.num_heads, self.head_dim) |
|
key = self._split_heads(key, self.num_heads, self.head_dim) |
|
value = self._split_heads(value, self.num_heads, self.head_dim) |
|
|
|
if layer_past is not None: |
|
past_key, past_value = layer_past |
|
key = torch.cat((past_key, key), dim=-2) |
|
value = torch.cat((past_value, value), dim=-2) |
|
|
|
if use_cache is True: |
|
present = (key, value) |
|
else: |
|
present = None |
|
|
|
batch_size, head_num, k_seq_len, head_features = key.shape |
|
_, _, q_seq_len, _ = query.shape |
|
query_offset = k_seq_len - q_seq_len |
|
if rotary_embedding is not None: |
|
query = query.contiguous().view(batch_size * head_num, q_seq_len, head_features) |
|
key = key.contiguous().view(batch_size * head_num, k_seq_len, head_features) |
|
|
|
|
|
if self.rotary_use_xpos: |
|
|
|
query, key = rotary_embedding.rotate_queries_and_keys(query, key) |
|
else: |
|
query = rotary_embedding.rotate_queries_or_keys(query, offset=query_offset) |
|
key = rotary_embedding.rotate_queries_or_keys(key) |
|
|
|
query = query.view(batch_size, head_num, q_seq_len, head_features) |
|
key = key.view(batch_size, head_num, k_seq_len, head_features) |
|
|
|
if self.reorder_and_upcast_attn and not self.use_flash_attn: |
|
attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask) |
|
else: |
|
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) |
|
attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim) |
|
attn_output = self.c_proj(attn_output) |
|
attn_output = self.resid_dropout(attn_output) |
|
outputs = (attn_output, present) |
|
if output_attentions: |
|
outputs += (attn_weights,) |
|
|
|
return outputs |
|
|
|
|
|
class TELECHATMLP(nn.Module): |
|
def __init__(self, intermediate_size, config): |
|
super().__init__() |
|
embed_dim = config.hidden_size |
|
if config.activation_function=='silu': |
|
up_intermediate_size = 2 * intermediate_size |
|
else: |
|
up_intermediate_size = intermediate_size |
|
self.c_fc = Conv1D(up_intermediate_size, embed_dim, bias=config.add_bias_linear) |
|
self.c_proj = Conv1D(embed_dim, intermediate_size, bias=config.add_bias_linear) |
|
if config.activation_function=='silu': |
|
def swiglu(x): |
|
x = torch.chunk(x, 2, dim=-1) |
|
return F.silu(x[0]) * x[1] |
|
self.act = swiglu |
|
else: |
|
self.act = ACT2FN[config.activation_function] |
|
self.dropout = nn.Dropout(config.resid_pdrop) |
|
|
|
def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor: |
|
hidden_states = self.c_fc(hidden_states) |
|
|
|
|
|
hidden_states = self.act(hidden_states) |
|
|
|
hidden_states = self.c_proj(hidden_states) |
|
hidden_states = self.dropout(hidden_states) |
|
return hidden_states |
|
|
|
|
|
class TELECHATBlock(nn.Module): |
|
def __init__(self, config, layer_idx=None): |
|
super().__init__() |
|
LayerNorm = nn.LayerNorm if not config.use_RMSNorm else RMSNorm |
|
hidden_size = config.hidden_size |
|
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size |
|
self.layer_idx = layer_idx |
|
self.ln_1 = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) |
|
self.attn = TELECHATAttention(config, layer_idx=layer_idx) |
|
self.ln_2 = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) |
|
self.mlp = TELECHATMLP(inner_dim, config) |
|
|
|
def forward( |
|
self, |
|
hidden_states: Optional[Tuple[torch.FloatTensor]], |
|
layer_past: Optional[Tuple[torch.Tensor]] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
encoder_hidden_states: Optional[torch.Tensor] = None, |
|
encoder_attention_mask: Optional[torch.FloatTensor] = None, |
|
rotary_embedding: Optional[RotaryEmbedding] = None, |
|
use_cache: Optional[bool] = False, |
|
output_attentions: Optional[bool] = False, |
|
) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]: |
|
residual = hidden_states |
|
hidden_states = self.ln_1(hidden_states) |
|
|
|
attn_outputs = self.attn( |
|
hidden_states, |
|
layer_past=layer_past, |
|
attention_mask=attention_mask, |
|
head_mask=head_mask, |
|
rotary_embedding=rotary_embedding, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions |
|
) |
|
attn_output = attn_outputs[0] |
|
outputs = attn_outputs[1:] |
|
|
|
hidden_states = attn_output + residual |
|
|
|
residual = hidden_states |
|
hidden_states = self.ln_2(hidden_states) |
|
feed_forward_hidden_states = self.mlp(hidden_states) |
|
|
|
hidden_states = residual + feed_forward_hidden_states |
|
if use_cache: |
|
outputs = (hidden_states,) + outputs |
|
else: |
|
outputs = (hidden_states,) + outputs[1:] |
|
|
|
|
|
return outputs |
|
|
|
|
|
class TELECHATPretrainedModel(PreTrainedModel): |
|
""" |
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained |
|
models. |
|
""" |
|
|
|
config_class = TELECHATConfig |
|
load_tf_weights = None |
|
base_model_prefix = "transformer" |
|
is_parallelizable = True |
|
supports_gradient_checkpointing = True |
|
_no_split_modules = ["TELECHATBlock"] |
|
|
|
def __init__(self, *inputs, **kwargs): |
|
super().__init__(*inputs, **kwargs) |
|
|
|
def _init_weights(self, module): |
|
"""Initialize the weights.""" |
|
if isinstance(module, (nn.Linear, Conv1D)): |
|
|
|
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
|
if module.bias is not None: |
|
module.bias.data.zero_() |
|
elif isinstance(module, nn.Embedding): |
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
|
if module.padding_idx is not None: |
|
module.weight.data[module.padding_idx].zero_() |
|
elif isinstance(module, nn.LayerNorm) or isinstance(module, RMSNorm): |
|
module.bias.data.zero_() |
|
module.weight.data.fill_(1.0) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
for name, p in module.named_parameters(): |
|
if name == "c_proj.weight": |
|
|
|
p.data.normal_(mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer))) |
|
|
|
def _set_gradient_checkpointing(self, module, value=False): |
|
if isinstance(module, TELECHATTransformer): |
|
module.gradient_checkpointing = value |
|
|
|
|
|
class TELECHATTransformer(TELECHATPretrainedModel): |
|
_keys_to_ignore_on_load_missing = ["attn.masked_bias"] |
|
|
|
def __init__(self, config): |
|
super().__init__(config) |
|
|
|
self.embed_dim = config.hidden_size |
|
|
|
self.relative_encoding = config.relative_encoding |
|
self.wte = nn.Embedding(config.vocab_size, self.embed_dim) |
|
|
|
self.use_mup = config.use_mup |
|
if self.use_mup: |
|
self.input_mult = config.input_mult |
|
|
|
if self.relative_encoding is None: |
|
self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim) |
|
elif self.relative_encoding == 'rotary': |
|
pe_dim = config.n_embd // config.n_head |
|
self.wpe = RotaryEmbedding(pe_dim, |
|
use_xpos=config.rotary_use_xpos, |
|
xpos_scale_base=config.rotary_xpos_scale_base, |
|
theta=config.rotary_theta |
|
) |
|
|
|
else: |
|
raise RuntimeError( |
|
f'Unknown relative positional encoding type: `relative_encoding`={self.relative_encoding}') |
|
self.drop = nn.Dropout(config.embd_pdrop) |
|
self.h = nn.ModuleList([TELECHATBlock(config, layer_idx=i + 1) for i in range(config.num_hidden_layers)]) |
|
LayerNorm = nn.LayerNorm if not config.use_RMSNorm else RMSNorm |
|
self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) |
|
|
|
|
|
self.model_parallel = False |
|
self.device_map = None |
|
self.gradient_checkpointing = False |
|
|
|
|
|
self.post_init() |
|
|
|
|
|
def parallelize(self, device_map=None): |
|
|
|
self.device_map = ( |
|
get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map |
|
) |
|
assert_device_map(self.device_map, len(self.h)) |
|
self.model_parallel = True |
|
self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys())) |
|
self.last_device = "cuda:" + str(max(self.device_map.keys())) |
|
self.wte = self.wte.to(self.first_device) |
|
self.wpe = self.wpe.to(self.first_device) |
|
|
|
for k, v in self.device_map.items(): |
|
for block in v: |
|
cuda_device = "cuda:" + str(k) |
|
self.h[block] = self.h[block].to(cuda_device) |
|
|
|
self.ln_f = self.ln_f.to(self.last_device) |
|
|
|
def deparallelize(self): |
|
self.model_parallel = False |
|
self.device_map = None |
|
self.first_device = "cpu" |
|
self.last_device = "cpu" |
|
self.wte = self.wte.to("cpu") |
|
self.wpe = self.wpe.to("cpu") |
|
for index in range(len(self.h)): |
|
self.h[index] = self.h[index].to("cpu") |
|
self.ln_f = self.ln_f.to("cpu") |
|
torch.cuda.empty_cache() |
|
|
|
def get_input_embeddings(self): |
|
return self.wte |
|
|
|
def set_input_embeddings(self, new_embeddings): |
|
self.wte = new_embeddings |
|
|
|
def _prune_heads(self, heads_to_prune): |
|
""" |
|
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} |
|
""" |
|
for layer, heads in heads_to_prune.items(): |
|
self.h[layer].attn.prune_heads(heads) |
|
|
|
def forward( |
|
self, |
|
input_ids: Optional[torch.LongTensor] = None, |
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
token_type_ids: Optional[torch.LongTensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
encoder_hidden_states: Optional[torch.Tensor] = None, |
|
encoder_attention_mask: Optional[torch.FloatTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: |
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions |
|
output_hidden_states = ( |
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
|
) |
|
use_cache = use_cache if use_cache is not None else self.config.use_cache |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
if input_ids is not None and inputs_embeds is not None: |
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") |
|
elif input_ids is not None: |
|
input_shape = input_ids.size() |
|
input_ids = input_ids.view(-1, input_shape[-1]) |
|
batch_size = input_ids.shape[0] |
|
elif inputs_embeds is not None: |
|
input_shape = inputs_embeds.size()[:-1] |
|
batch_size = inputs_embeds.shape[0] |
|
else: |
|
raise ValueError("You have to specify either input_ids or inputs_embeds") |
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device |
|
|
|
if token_type_ids is not None: |
|
token_type_ids = token_type_ids.view(-1, input_shape[-1]) |
|
if position_ids is not None: |
|
position_ids = position_ids.view(-1, input_shape[-1]) |
|
|
|
if past_key_values is None: |
|
past_length = 0 |
|
past_key_values = tuple([None] * len(self.h)) |
|
else: |
|
past_length = past_key_values[0][0].size(-2) |
|
if position_ids is None: |
|
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) |
|
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) |
|
|
|
|
|
if attention_mask is not None: |
|
if batch_size <= 0: |
|
raise ValueError("batch_size has to be defined and > 0") |
|
attention_mask = attention_mask.view(batch_size, -1) |
|
|
|
|
|
|
|
|
|
|
|
attention_mask = attention_mask[:, None, None, :] |
|
|
|
|
|
|
|
|
|
|
|
|
|
attention_mask = attention_mask.to(dtype=self.dtype) |
|
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
encoder_attention_mask = None |
|
|
|
|
|
|
|
|
|
|
|
head_mask = self.get_head_mask(head_mask, self.config.n_layer) |
|
|
|
if inputs_embeds is None: |
|
inputs_embeds = self.wte(input_ids) |
|
|
|
|
|
if self.use_mup: |
|
inputs_embeds = inputs_embeds * self.input_mult |
|
if self.relative_encoding is None: |
|
position_embeds = self.wpe(position_ids) |
|
hidden_states = inputs_embeds + position_embeds |
|
elif self.relative_encoding == 'rotary': |
|
hidden_states = inputs_embeds |
|
if token_type_ids is not None: |
|
token_type_embeds = self.wte(token_type_ids) |
|
hidden_states = hidden_states + token_type_embeds |
|
hidden_states = self.drop(hidden_states) |
|
|
|
output_shape = input_shape + (hidden_states.size(-1),) |
|
|
|
presents = () if use_cache else None |
|
all_self_attentions = () if output_attentions else None |
|
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None |
|
all_hidden_states = () if output_hidden_states else None |
|
|
|
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): |
|
|
|
|
|
if self.model_parallel: |
|
torch.cuda.set_device(hidden_states.device) |
|
|
|
if layer_past is not None: |
|
layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past) |
|
|
|
if attention_mask is not None: |
|
attention_mask = attention_mask.to(hidden_states.device) |
|
if isinstance(head_mask, torch.Tensor): |
|
head_mask = head_mask.to(hidden_states.device) |
|
if output_hidden_states: |
|
all_hidden_states = all_hidden_states + (hidden_states,) |
|
|
|
if self.gradient_checkpointing and self.training: |
|
|
|
if use_cache: |
|
logger.warning( |
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." |
|
) |
|
use_cache = False |
|
|
|
def create_custom_forward(module): |
|
def custom_forward(*inputs): |
|
|
|
return module(*inputs, use_cache, output_attentions) |
|
|
|
return custom_forward |
|
|
|
outputs = torch.utils.checkpoint.checkpoint( |
|
create_custom_forward(block), |
|
hidden_states, |
|
None, |
|
attention_mask, |
|
head_mask[i], |
|
encoder_hidden_states, |
|
encoder_attention_mask, |
|
) |
|
else: |
|
outputs = block( |
|
hidden_states, |
|
layer_past=layer_past, |
|
attention_mask=attention_mask, |
|
head_mask=head_mask[i], |
|
encoder_hidden_states=encoder_hidden_states, |
|
encoder_attention_mask=encoder_attention_mask, |
|
rotary_embedding=self.wpe if self.relative_encoding == 'rotary' else None, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions |
|
) |
|
|
|
hidden_states = outputs[0] |
|
if use_cache is True: |
|
presents = presents + (outputs[1],) |
|
|
|
if output_attentions: |
|
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) |
|
|
|
|
|
|
|
|
|
if self.model_parallel: |
|
for k, v in self.device_map.items(): |
|
if i == v[-1] and "cuda:" + str(k) != self.last_device: |
|
hidden_states = hidden_states.to("cuda:" + str(k + 1)) |
|
|
|
hidden_states = self.ln_f(hidden_states) |
|
|
|
hidden_states = hidden_states.view(output_shape) |
|
|
|
if output_hidden_states: |
|
all_hidden_states = all_hidden_states + (hidden_states,) |
|
|
|
if not return_dict: |
|
return tuple( |
|
v |
|
for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions] |
|
if v is not None |
|
) |
|
|
|
return BaseModelOutputWithPastAndCrossAttentions( |
|
last_hidden_state=hidden_states, |
|
past_key_values=presents, |
|
hidden_states=all_hidden_states, |
|
attentions=all_self_attentions, |
|
cross_attentions=all_cross_attentions, |
|
) |
|
|
|
|
|
class TELECHAT(TELECHATPretrainedModel): |
|
_keys_to_ignore_on_load_missing = [r"attn.masked_bias", r"attn.bias", r"lm_head.weight"] |
|
|
|
def __init__(self, config): |
|
super().__init__(config) |
|
self.transformer = TELECHATTransformer(config) |
|
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) |
|
self.use_mup = config.use_mup |
|
if self.use_mup: |
|
self.mup_scale_factor = config.mup_scale_factor |
|
self.output_mult = config.output_mult / self.mup_scale_factor |
|
|
|
|
|
self.enable_flash_attn(config.enable_flash_attn) |
|
|
|
|
|
self.model_parallel = False |
|
self.device_map = None |
|
|
|
|
|
self.post_init() |
|
def enable_flash_attn(self, enabled: bool): |
|
for block in self.transformer.h: |
|
block.attn.use_flash_attn = enabled |
|
print(f"TELECHAT flash attention {'enabled' if enabled else 'disabled'}") |
|
|
|
def set_max_positions(self, max_positions): |
|
for layer in self.transformer.h: |
|
device = layer.ln_1.weight.device |
|
layer.attn.set_max_positions(max_positions, device=device) |
|
|
|
def parallelize(self, device_map=None): |
|
self.device_map = ( |
|
get_device_map(len(self.transformer.h), range(torch.cuda.device_count())) |
|
if device_map is None |
|
else device_map |
|
) |
|
assert_device_map(self.device_map, len(self.transformer.h)) |
|
self.transformer.parallelize(self.device_map) |
|
self.lm_head = self.lm_head.to(self.transformer.first_device) |
|
self.model_parallel = True |
|
|
|
def deparallelize(self): |
|
self.transformer.deparallelize() |
|
self.transformer = self.transformer.to("cpu") |
|
self.lm_head = self.lm_head.to("cpu") |
|
self.model_parallel = False |
|
torch.cuda.empty_cache() |
|
|
|
def get_output_embeddings(self): |
|
return self.lm_head |
|
|
|
def set_output_embeddings(self, new_embeddings): |
|
self.lm_head = new_embeddings |
|
|
|
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs): |
|
token_type_ids = kwargs.get("token_type_ids", None) |
|
|
|
if past_key_values: |
|
input_ids = input_ids[:, -1].unsqueeze(-1) |
|
if token_type_ids is not None: |
|
token_type_ids = token_type_ids[:, -1].unsqueeze(-1) |
|
|
|
attention_mask = kwargs.get("attention_mask", None) |
|
position_ids = kwargs.get("position_ids", None) |
|
|
|
if attention_mask is not None and position_ids is None: |
|
|
|
position_ids = attention_mask.long().cumsum(-1) - 1 |
|
position_ids.masked_fill_(attention_mask == 0, 1) |
|
if past_key_values: |
|
position_ids = position_ids[:, -1].unsqueeze(-1) |
|
else: |
|
position_ids = None |
|
return { |
|
"input_ids": input_ids, |
|
"past_key_values": past_key_values, |
|
"use_cache": kwargs.get("use_cache"), |
|
"position_ids": position_ids, |
|
"attention_mask": attention_mask, |
|
"token_type_ids": token_type_ids, |
|
} |
|
|
|
def forward( |
|
self, |
|
input_ids: Optional[torch.LongTensor] = None, |
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
token_type_ids: Optional[torch.LongTensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
encoder_hidden_states: Optional[torch.Tensor] = None, |
|
encoder_attention_mask: Optional[torch.FloatTensor] = None, |
|
labels: Optional[torch.LongTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast]: |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
transformer_outputs = self.transformer( |
|
input_ids, |
|
past_key_values=past_key_values, |
|
attention_mask=attention_mask, |
|
token_type_ids=token_type_ids, |
|
position_ids=position_ids, |
|
head_mask=head_mask, |
|
inputs_embeds=inputs_embeds, |
|
encoder_hidden_states=encoder_hidden_states, |
|
encoder_attention_mask=encoder_attention_mask, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict |
|
) |
|
hidden_states = transformer_outputs[0] |
|
|
|
|
|
if self.model_parallel: |
|
torch.cuda.set_device(self.transformer.first_device) |
|
hidden_states = hidden_states.to(self.lm_head.weight.device) |
|
|
|
lm_logits = self.lm_head(hidden_states) |
|
|
|
if self.use_mup: |
|
lm_logits = lm_logits * self.output_mult |
|
|
|
loss = None |
|
if labels is not None: |
|
|
|
shift_logits = lm_logits[..., :-1, :].contiguous() |
|
shift_labels = labels[..., 1:].contiguous() |
|
|
|
loss_fct = nn.CrossEntropyLoss() |
|
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) |
|
|
|
if not return_dict: |
|
output = (lm_logits,) + transformer_outputs[1:] |
|
return ((loss,) + output) if loss is not None else output |
|
|
|
return CausalLMOutputWithCrossAttentions( |
|
loss=loss, |
|
logits=lm_logits, |
|
past_key_values=transformer_outputs.past_key_values, |
|
hidden_states=transformer_outputs.hidden_states, |
|
attentions=transformer_outputs.attentions, |
|
cross_attentions=transformer_outputs.cross_attentions, |
|
) |
|
|
|
def chat(self,tokenizer, question, history_input_list, history_output_list,generation_config): |
|
''' |
|
:param question: 当前问题 |
|
:param history_input_list: 历史问题列表, list of strings |
|
:param history_output_list: 历史回答列表, list of string |
|
:return: response |
|
''' |
|
|
|
inputs = "" |
|
assert len(history_output_list) == len(history_output_list) |
|
for i in range(len(history_input_list)): |
|
inputs += "<_user>" + history_input_list[i] + "<_bot>" + history_output_list[i] + "<_end>" |
|
inputs += "<_user>" + question + "<_bot>" |
|
print("input:", inputs) |
|
input_ids = tokenizer.encode(inputs, |
|
return_tensors="pt" |
|
) |
|
if len(input_ids[0]) >= 2000: |
|
input_ids = input_ids[:, -2000:] |
|
input_ids = input_ids.to(0) |
|
output = self.generate(input_ids,generation_config) |
|
response = tokenizer.decode(output[0].cpu().numpy().tolist()).split('<_bot>')[-1].split('</s>')[0] |
|
return response |
|
|
|
@staticmethod |
|
def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]: |
|
""" |
|
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or |
|
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct |
|
beam_idx at every generation step. |
|
""" |
|
return tuple( |
|
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) |
|
for layer_past in past |
|
) |
|
|