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Merge pull request #20 from kmn1024/stablelm
Add support for StableLM 3b 4e1t model
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| Original file line number | Diff line number | Diff line change |
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| from .pos_shift import stablelm_epoch_pos_shift_attention_forward |
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| Original file line number | Diff line number | Diff line change |
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| """ | ||
| Adapted from https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/modeling_stablelm_epoch.py | ||
| """ | ||
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| from typing import Optional, Tuple | ||
| import math | ||
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| import torch | ||
| from torch import nn | ||
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| __all__ = ["stablelm_epoch_pos_shift_attention_forward"] | ||
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| def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: | ||
| """ | ||
| This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, | ||
| num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) | ||
| """ | ||
| batch, num_key_value_heads, slen, head_dim = hidden_states.shape | ||
| if n_rep == 1: | ||
| return hidden_states | ||
| hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) | ||
| return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) | ||
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| def rotate_half(x: torch.Tensor): | ||
| """Rotates half the hidden dims of the input.""" | ||
| x1, x2 = torch.chunk(x, 2, dim=-1) | ||
| return torch.cat((-x2, x1), dim=-1) | ||
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| def apply_rotary_pos_emb_single(x, cos, sin, position_ids): | ||
| # The first two dimensions of cos and sin are always 1, so we can `squeeze` them. | ||
| cos = cos.squeeze(1).squeeze(0) # [seq_len, dim] | ||
| sin = sin.squeeze(1).squeeze(0) # [seq_len, dim] | ||
| cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] | ||
| sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] | ||
| x_embed = (x * cos) + (rotate_half(x) * sin) | ||
| return x_embed | ||
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| def stablelm_epoch_pos_shift_attention_forward( | ||
| self, | ||
| hidden_states: torch.FloatTensor, | ||
| attention_mask: torch.FloatTensor, | ||
| position_ids: torch.LongTensor, | ||
| past_key_value: Optional[Tuple[torch.Tensor]] = None, | ||
| output_attentions: Optional[bool] = False, | ||
| use_cache: Optional[bool] = False, | ||
| ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: | ||
| bsz, q_len, _ = hidden_states.size() | ||
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| query_states = self.q_proj(hidden_states) | ||
| key_states = self.k_proj(hidden_states) | ||
| value_states = self.v_proj(hidden_states) | ||
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| query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) | ||
| key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) | ||
| value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) | ||
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| query_rot = query_states[..., : self.rotary_ndims] | ||
| query_pass = query_states[..., self.rotary_ndims :] | ||
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| kv_seq_len = key_states.shape[-2] | ||
| if past_key_value is not None: | ||
| kv_seq_len += past_key_value[0].shape[-2] | ||
| cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) | ||
| query_states = apply_rotary_pos_emb_single(query_rot, cos, sin, position_ids) | ||
| # [batch_size, num_heads, seq_len, head_dim] | ||
| query_states = torch.cat((query_states, query_pass), dim=-1) | ||
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| if past_key_value is not None: | ||
| # Reuse k, v, self_attention | ||
| key_states = torch.cat((past_key_value[0], key_states), dim=2) | ||
| value_states = torch.cat((past_key_value[1], value_states), dim=2) | ||
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| past_key_value = (key_states, value_states) if use_cache else None | ||
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| key_rot = key_states[..., : self.rotary_ndims] | ||
| key_pass = key_states[..., self.rotary_ndims :] | ||
| key_position_ids = torch.arange(kv_seq_len, device=position_ids.device).unsqueeze(0) | ||
| key_states = apply_rotary_pos_emb_single(key_rot, cos, sin, key_position_ids) | ||
| # [batch_size, num_heads, seq_len, head_dim] | ||
| key_states = torch.cat((key_states, key_pass), dim=-1) | ||
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| # Repeat k/v heads if n_kv_heads < n_heads | ||
| key_states = repeat_kv(key_states, self.num_key_value_groups) | ||
| 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, q_len, kv_seq_len): | ||
| raise ValueError( | ||
| f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" | ||
| f" {attn_weights.size()}" | ||
| ) | ||
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| if attention_mask is not None: | ||
| if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): | ||
| raise ValueError( | ||
| f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" | ||
| ) | ||
| attn_weights = attn_weights + attention_mask | ||
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| # Upcast attention to fp32 | ||
| attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) | ||
| attn_output = torch.matmul(attn_weights, value_states) | ||
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| if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): | ||
| raise ValueError( | ||
| f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" | ||
| f" {attn_output.size()}" | ||
| ) | ||
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| # Merge heads | ||
| attn_output = attn_output.transpose(1, 2).contiguous() | ||
| attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) | ||
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| # Final linear projection | ||
| attn_output = self.o_proj(attn_output) | ||
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| if not output_attentions: | ||
| attn_weights = None | ||
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| return attn_output, attn_weights, past_key_value |
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