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https://github.com/nod-ai/AMD-SHARK-Studio.git
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ab01f0f048
* Add H2OGPT * Add UI tab for h2ogpt * Add source files from h2ogpt * Add the rest of the files * Add h2ogpt support * Add SHARK Compilation support for langchain model for cli mode --------- Co-authored-by: George Petterson <gpetters@protonmail.com>
125 lines
4.1 KiB
Python
125 lines
4.1 KiB
Python
from typing import List, Optional, Tuple
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import torch
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import transformers
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from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
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from einops import rearrange
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from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
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from flash_attn.bert_padding import unpad_input, pad_input
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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.Tensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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) -> Tuple[
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torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]
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]:
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"""Input shape: Batch x Time x Channel
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attention_mask: [bsz, q_len]
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"""
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bsz, q_len, _ = hidden_states.size()
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query_states = (
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self.q_proj(hidden_states)
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.view(bsz, q_len, self.num_heads, self.head_dim)
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.transpose(1, 2)
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)
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key_states = (
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self.k_proj(hidden_states)
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.view(bsz, q_len, self.num_heads, self.head_dim)
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.transpose(1, 2)
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)
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value_states = (
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self.v_proj(hidden_states)
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.view(bsz, q_len, self.num_heads, self.head_dim)
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.transpose(1, 2)
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)
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# [bsz, q_len, nh, hd]
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# [bsz, nh, q_len, hd]
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kv_seq_len = key_states.shape[-2]
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assert past_key_value is None, "past_key_value is not supported"
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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(
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query_states, key_states, cos, sin, position_ids
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)
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# [bsz, nh, t, hd]
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assert not output_attentions, "output_attentions is not supported"
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assert not use_cache, "use_cache is not supported"
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# Flash attention codes from
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# https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py
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# transform the data into the format required by flash attention
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qkv = torch.stack(
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[query_states, key_states, value_states], dim=2
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) # [bsz, nh, 3, q_len, hd]
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qkv = qkv.transpose(1, 3) # [bsz, q_len, 3, nh, hd]
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# We have disabled _prepare_decoder_attention_mask in LlamaModel
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# the attention_mask should be the same as the key_padding_mask
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key_padding_mask = attention_mask
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if key_padding_mask is None:
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qkv = rearrange(qkv, "b s ... -> (b s) ...")
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max_s = q_len
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cu_q_lens = torch.arange(
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0,
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(bsz + 1) * q_len,
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step=q_len,
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dtype=torch.int32,
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device=qkv.device,
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)
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output = flash_attn_unpadded_qkvpacked_func(
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qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
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)
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output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
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else:
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nheads = qkv.shape[-2]
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x = rearrange(qkv, "b s three h d -> b s (three h d)")
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x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
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x_unpad = rearrange(
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x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads
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)
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output_unpad = flash_attn_unpadded_qkvpacked_func(
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x_unpad, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
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)
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output = rearrange(
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pad_input(
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rearrange(output_unpad, "nnz h d -> nnz (h d)"),
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indices,
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bsz,
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q_len,
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),
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"b s (h d) -> b s h d",
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h=nheads,
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)
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return self.o_proj(rearrange(output, "b s h d -> b s (h d)")), None, None
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# Disable the transformation of the attention mask in LlamaModel as the flash attention
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# requires the attention mask to be the same as the key_padding_mask
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def _prepare_decoder_attention_mask(
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self, attention_mask, input_shape, inputs_embeds, past_key_values_length
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):
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# [bsz, seq_len]
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return attention_mask
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def replace_llama_attn_with_flash_attn():
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print(
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"Replacing original LLaMa attention with flash attention", flush=True
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)
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transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = (
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_prepare_decoder_attention_mask
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)
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transformers.models.llama.modeling_llama.LlamaAttention.forward = forward
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