wehub-resource-sync
398 lines
14 KiB
Python
398 lines
14 KiB
Python
from functools import partial
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from typing import Optional, Tuple, Union
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import jittor as jt
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import jittor.nn as nn
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from jittor import Module
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from .utils import NewGELUActivation
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from .utils import (fixed_pos_embedding, apply_rotary_pos_emb, _init_weights,
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get_head_mask)
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class MossAttention(Module):
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def __init__(self, config):
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super(MossAttention, self).__init__()
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max_positions = config.n_positions
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self.register_buffer(
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"causal_mask",
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jt.tril(jt.ones((max_positions, max_positions), dtype=jt.bool)).view(
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1, 1, max_positions, max_positions
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),
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)
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self.attn_dropout = nn.Dropout(config.attn_pdrop)
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self.resid_dropout = nn.Dropout(config.resid_pdrop)
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self.embed_dim = config.n_embd
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self.num_attention_heads = config.n_head
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self.head_dim = self.embed_dim // self.num_attention_heads
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if self.head_dim * self.num_attention_heads != self.embed_dim:
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raise ValueError(
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f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and"
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f" `num_attention_heads`: {self.num_attention_heads})."
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)
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self.scale_attn = jt.sqrt(jt.float32(self.head_dim))
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self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3, bias=False)
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jt.float16
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self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
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self.rotary_dim = None
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if config.rotary_dim is not None:
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self.rotary_dim = config.rotary_dim
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def _split_heads(self, x, n_head, dim_head, mp_num):
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reshaped = x.reshape(x.shape[:-1] + (n_head // mp_num, dim_head))
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reshaped = reshaped.reshape(x.shape[:-2] + (-1,) + reshaped.shape[-1:])
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return reshaped
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def _merge_heads(self, tensor, num_attention_heads, attn_head_size):
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"""
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Merges attn_head_size dim and num_attn_heads dim into n_ctx
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"""
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if len(tensor.shape) == 5:
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tensor = tensor.permute(0, 1, 3, 2, 4).contiguous()
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elif len(tensor.shape) == 4:
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tensor = tensor.permute(0, 2, 1, 3).contiguous()
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else:
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raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")
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new_shape = tensor.size()[:-2] + (num_attention_heads * attn_head_size,)
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return tensor.view(new_shape)
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def _attn(
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self,
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query,
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key,
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value,
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attention_mask=None,
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head_mask=None,
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):
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# compute causal mask from causal mask buffer
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query_length, key_length = query.size(-2), key.size(-2)
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causal_mask = self.causal_mask[:, :, key_length - query_length : key_length, :key_length]
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# Keep the attention weights computation in fp32 to avoid overflow issues
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query = query.to('float32')
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key = key.to('float32')
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attn_weights = jt.matmul(query, key.transpose(-1, -2))
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attn_weights = attn_weights / self.scale_attn
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mask_value = -3.4e38 # torch.finfo(attn_weights.dtype).min)
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mask_value = jt.Var(mask_value).type_as(attn_weights)
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attn_weights = jt.where(causal_mask, attn_weights, mask_value)
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if attention_mask is not None:
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# Apply the attention mask
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attn_weights = attn_weights + attention_mask
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attn_weights = nn.Softmax(dim=-1)(attn_weights)
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attn_weights = attn_weights.to(value.dtype)
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attn_weights = self.attn_dropout(attn_weights)
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# Mask heads if we want to
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if head_mask is not None:
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attn_weights = attn_weights * head_mask
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attn_output = jt.matmul(attn_weights, value.float())
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if jt.flags.amp_level >= 1:
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attn_output = attn_output.half()
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return attn_output, attn_weights
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def execute(
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self,
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hidden_states: Optional[jt.Var],
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attention_mask: Optional[jt.Var] = None,
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layer_past: Optional[Tuple[jt.Var]] = None,
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head_mask: Optional[jt.Var] = None,
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use_cache: Optional[bool] = False,
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) -> Union[
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Tuple[jt.Var, Tuple[jt.Var]],
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Optional[Tuple[jt.Var, Tuple[jt.Var], Tuple[jt.Var, ...]]],
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]:
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qkv = self.qkv_proj(hidden_states)
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mp_num = 4
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qkv_split = qkv.reshape(qkv.shape[:-1] + (mp_num, -1))
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local_dim = self.head_dim * self.num_attention_heads // mp_num
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query, value, key = jt.split(qkv_split, local_dim, dim=-1)
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query = self._split_heads(query, self.num_attention_heads, self.head_dim, mp_num=mp_num)
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key = self._split_heads(key, self.num_attention_heads, self.head_dim, mp_num=mp_num)
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value = self._split_heads(value, self.num_attention_heads, self.head_dim, mp_num=mp_num)
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value = value.permute(0, 2, 1, 3)
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seq_len = key.shape[1]
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offset = 0
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if layer_past is not None:
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offset = layer_past[0].shape[-2]
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seq_len += offset
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if self.rotary_dim is not None:
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k_rot = key[:, :, :, : self.rotary_dim]
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k_pass = key[:, :, :, self.rotary_dim :]
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q_rot = query[:, :, :, : self.rotary_dim]
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q_pass = query[:, :, :, self.rotary_dim :]
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sincos = fixed_pos_embedding(k_rot, 1, seq_len=seq_len)
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k_rot = apply_rotary_pos_emb(k_rot, sincos, offset=offset)
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q_rot = apply_rotary_pos_emb(q_rot, sincos, offset=offset)
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key = jt.cat([k_rot, k_pass], dim=-1)
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query = jt.cat([q_rot, q_pass], dim=-1)
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else:
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sincos = fixed_pos_embedding(key, 1, seq_len=seq_len)
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key = apply_rotary_pos_emb(key, sincos, offset=offset)
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query = apply_rotary_pos_emb(query, sincos, offset=offset)
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key = key.permute(0, 2, 1, 3)
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query = query.permute(0, 2, 1, 3)
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if layer_past is not None:
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past_key = layer_past[0]
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past_value = layer_past[1]
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key = jt.cat((past_key, key), dim=-2)
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value = jt.cat((past_value, value), dim=-2)
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if use_cache is True:
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present = (key, value)
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else:
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present = None
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# compute self-attention: V x Softmax(QK^T)
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attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
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attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim)
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attn_output = self.out_proj(attn_output)
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attn_output = self.resid_dropout(attn_output)
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outputs = (attn_output, present)
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return outputs # a, present
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class MossMLP(Module):
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def __init__(self, intermediate_size, config):
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# in MLP: intermediate_size= 4 * embed_dim
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super(MossMLP, self).__init__()
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embed_dim = config.n_embd
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self.fc_in = nn.Linear(embed_dim, intermediate_size)
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self.fc_out = nn.Linear(intermediate_size, embed_dim)
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self.act = NewGELUActivation()
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self.dropout = nn.Dropout(config.resid_pdrop)
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def execute(self, hidden_states: Optional[jt.Var]) -> jt.Var:
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hidden_states = self.fc_in(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states = self.fc_out(hidden_states)
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hidden_states = self.dropout(hidden_states)
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return hidden_states
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class MossBlock(Module):
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def __init__(self, config):
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super(MossBlock, self).__init__()
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self.config = config
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inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
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self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
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self.attn = MossAttention(config)
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self.mlp = MossMLP(inner_dim, config)
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def execute(
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self,
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hidden_states: Optional[jt.Var],
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layer_past: Optional[Tuple[jt.Var]] = None,
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attention_mask: Optional[jt.Var] = None,
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head_mask: Optional[jt.Var] = None,
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use_cache: Optional[bool] = False,
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) -> Union[Tuple[jt.Var], Optional[Tuple[jt.Var, Tuple[jt.Var, ...]]]]:
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residual = hidden_states
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hidden_states = self.ln_1(hidden_states)
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attn_outputs = self.attn(
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hidden_states,
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layer_past=layer_past,
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attention_mask=attention_mask,
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head_mask=head_mask,
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use_cache=use_cache
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)
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attn_output = attn_outputs[0] # output_attn: a, present
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outputs = attn_outputs[1:]
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feed_forward_hidden_states = self.mlp(hidden_states)
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hidden_states = attn_output + feed_forward_hidden_states + residual
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if use_cache:
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outputs = (hidden_states,) + outputs
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else:
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outputs = (hidden_states,) + outputs[1:]
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return outputs # hidden_states, present
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class MossModel(Module):
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def __init__(self, config):
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super(MossModel, self).__init__()
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self.config = config
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self.embed_dim = config.n_embd
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self.vocab_size = config.vocab_size
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self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
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self.drop = nn.Dropout(config.embd_pdrop)
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self.h = nn.ModuleList([MossBlock(config) for _ in range(config.n_layer)])
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self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
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self.rotary_dim = min(config.rotary_dim, config.n_ctx // config.n_head)
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self.gradient_checkpointing = False
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self.apply(partial(_init_weights, config))
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def execute(
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self,
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input_ids: Optional[jt.Var] = None,
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past_key_values: Optional[Tuple[Tuple[jt.Var]]] = None,
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attention_mask: Optional[jt.Var] = None,
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token_type_ids: Optional[jt.Var] = None,
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position_ids: Optional[jt.Var] = None,
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head_mask: Optional[jt.Var] = None,
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inputs_embeds: Optional[jt.Var] = None,
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use_cache: Optional[bool] = None,
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):
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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elif input_ids is not None:
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input_shape = input_ids.size()
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input_ids = input_ids.view(-1, input_shape[-1])
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batch_size = input_ids.shape[0]
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elif inputs_embeds is not None:
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input_shape = inputs_embeds.size()[:-1]
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batch_size = inputs_embeds.shape[0]
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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if token_type_ids is not None:
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token_type_ids = token_type_ids.view(-1, input_shape[-1])
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if position_ids is not None:
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position_ids = position_ids.view(-1, input_shape[-1])
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if past_key_values is None:
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past_length = 0
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past_key_values = tuple([None] * len(self.h))
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else:
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past_length = past_key_values[0][0].size(-2)
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if position_ids is None:
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position_ids = jt.arange(past_length, input_shape[-1] + past_length, dtype='int64')
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position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
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# Attention mask.
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if attention_mask is not None:
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if batch_size <= 0:
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raise ValueError("batch_size has to be defined and > 0")
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attention_mask = attention_mask.view(batch_size, -1)
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# [batch_size, 1, 1, to_seq_length]
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attention_mask = attention_mask[:, None, None, :]
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if jt.flags.amp_level >= 3:
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attention_mask = attention_mask.half() # fp16 compatibility
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attention_mask = (1.0 - attention_mask) * -65504.0
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else:
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# finfo.min
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attention_mask = (1.0 - attention_mask) * -3.402e38
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# n_layer x batch x num_attention_heads x N x N
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head_mask = get_head_mask(head_mask, self.config.n_layer)
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if inputs_embeds is None:
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inputs_embeds = self.wte(input_ids)
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hidden_states = inputs_embeds
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if token_type_ids is not None:
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token_type_embeds = self.wte(token_type_ids)
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hidden_states = hidden_states + token_type_embeds
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hidden_states = self.drop(hidden_states)
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output_shape = input_shape + (hidden_states.size(-1),)
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presents = () if use_cache else None
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for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
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outputs = block(
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hidden_states,
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layer_past=layer_past,
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attention_mask=attention_mask,
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head_mask=head_mask[i],
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use_cache=use_cache,
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)
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hidden_states = outputs[0]
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if use_cache is True:
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presents = presents + (outputs[1],)
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hidden_states = self.ln_f(hidden_states)
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hidden_states = hidden_states.view(output_shape)
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return hidden_states, presents
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class MossForCausalLM(Module):
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def __init__(self, config):
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super(MossForCausalLM, self).__init__()
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self.config = config
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self.transformer = MossModel(config)
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self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
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# Initialize weights and apply final processing
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self.apply(partial(_init_weights, config))
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def execute(
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self,
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input_ids: Optional[jt.Var] = None,
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past_key_values: Optional[Tuple[Tuple[jt.Var]]] = None,
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attention_mask: Optional[jt.Var] = None,
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token_type_ids: Optional[jt.Var] = None,
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position_ids: Optional[jt.Var] = None,
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head_mask: Optional[jt.Var] = None,
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inputs_embeds: Optional[jt.Var] = None,
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labels: Optional[jt.Var] = None,
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use_cache: Optional[bool] = None,
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):
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hidden_states, presents = self.transformer(
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input_ids,
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past_key_values=past_key_values,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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)
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lm_logits = self.lm_head(hidden_states).to('float32')
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loss = None
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if labels is not None:
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shift_logits = lm_logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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loss_fct = nn.CrossEntropyLoss()
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loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
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loss = loss.to(hidden_states.dtype)
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return dict(
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loss=loss,
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logits=lm_logits,
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past_key_values=presents
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)
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