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Qualcomm AI Engine Direct - Add llama sha transforming pass
Differential Revision: D64435128 Pull Request resolved: #6211
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219
examples/models/llama/source_transformation/attention.py
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| # Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| # All rights reserved. | ||
| # | ||
| # This source code is licensed under the BSD-style license found in the | ||
| # LICENSE file in the root directory of this source tree. | ||
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| # pyre-unsafe | ||
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| # Example script for exporting Llama2 to flatbuffer | ||
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| import math | ||
| from typing import List, Optional, Tuple | ||
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| import torch | ||
| from executorch.examples.models.llama.llama_transformer import Attention | ||
| from torch import nn | ||
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| def apply_rotary_emb_single( | ||
| x: torch.Tensor, freqs_cos: torch.Tensor, freqs_sin: torch.Tensor | ||
| ) -> torch.Tensor: | ||
| x_r, x_i = x[..., ::2], x[..., 1::2] | ||
|
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| x_out_r = x_r * freqs_cos - x_i * freqs_sin | ||
| x_out_i = x_r * freqs_sin + x_i * freqs_cos | ||
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| x_out = torch.cat([x_out_r, x_out_i], dim=-1) | ||
| return x_out | ||
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| class KVCacheSHA(torch.nn.Module): | ||
| def __init__( | ||
| self, | ||
| max_batch_size: int, | ||
| max_seq_length: int, | ||
| n_heads: int, | ||
| head_dim: int, | ||
| dtype=torch.float32, | ||
| ): | ||
| super().__init__() | ||
|
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||
| # a buffer per head | ||
| cache_shape = (max_batch_size, max_seq_length, head_dim) | ||
| for i in range(n_heads): | ||
| self.register_buffer( | ||
| f"past_k_caches_{i}", | ||
| torch.zeros(cache_shape, dtype=dtype, device="cpu"), | ||
| persistent=False, | ||
| ) | ||
| self.register_buffer( | ||
| f"past_v_caches_{i}", | ||
| torch.zeros(cache_shape, dtype=dtype, device="cpu"), | ||
| persistent=False, | ||
| ) | ||
|
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||
| def update( | ||
| self, | ||
| input_pos: torch.Tensor, | ||
| k_val: torch.Tensor, | ||
| v_val: torch.Tensor, | ||
| cache_idx: int, | ||
| ) -> Tuple[torch.Tensor, torch.Tensor]: | ||
| new_k = torch.ops.aten.index_put_( | ||
| getattr(self, f"past_k_caches_{cache_idx}"), [None, input_pos], k_val | ||
| ) | ||
| new_v = torch.ops.aten.index_put_( | ||
| getattr(self, f"past_v_caches_{cache_idx}"), [None, input_pos], v_val | ||
| ) | ||
| return new_k, new_v | ||
|
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| def get_cache(self, head_idx): | ||
| return getattr(self, f"past_k_caches_{head_idx}"), getattr( | ||
| self, f"past_v_caches_{head_idx}" | ||
| ) | ||
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| class SDPASHA(torch.nn.Module): | ||
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| def __init__( | ||
| self, | ||
| max_batch_size: int, | ||
| max_seq_length: int, | ||
| n_heads: int, | ||
| n_rep: int, | ||
| head_dim: int, | ||
| dim: int, | ||
| ): | ||
| super().__init__() | ||
| self.head_dim = head_dim | ||
| self.n_rep = n_rep | ||
| self.dim = dim | ||
| self.kv_cache = KVCacheSHA( | ||
| max_batch_size, max_seq_length, n_heads // n_rep, head_dim | ||
| ) | ||
| self.scale_factor = math.sqrt(head_dim) | ||
|
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| def forward( | ||
| self, | ||
| input_pos: torch.Tensor, | ||
| qs: List[torch.Tensor], | ||
| ks: List[torch.Tensor], | ||
| vs: List[torch.Tensor], | ||
| mask, | ||
| ): | ||
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| transpose_ks = [] | ||
| for i in range(len(ks)): | ||
| new_k, _ = self.kv_cache.update(input_pos, ks[i], vs[i], i) | ||
| transpose_ks.append(new_k.transpose(-2, -1).contiguous()) | ||
|
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| output = [] | ||
| for i, q in enumerate(qs): | ||
| cache_idx = i // self.n_rep | ||
| _, v = self.kv_cache.get_cache(cache_idx) | ||
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| attn_mask = mask[input_pos] | ||
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| attn_weight = q @ transpose_ks[cache_idx] / self.scale_factor | ||
| attn_weight += attn_mask | ||
| attn_weight = torch.softmax(attn_weight, dim=-1) | ||
| output.append(attn_weight @ v.contiguous()) | ||
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| return torch.cat(output, dim=-1) | ||
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| class AttentionSHA(nn.Module): | ||
| def __init__(self, attention_mha: nn.Module): | ||
| super().__init__() | ||
| if not attention_mha.use_kv_cache: | ||
| raise NotImplementedError("bert mode is not support") | ||
|
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| self.n_heads = attention_mha.n_heads | ||
| self.n_kv_heads = attention_mha.n_kv_heads | ||
| self.n_rep = self.n_heads // self.n_kv_heads | ||
| self.dim = attention_mha.dim | ||
| self.max_batch_size = attention_mha.max_batch_size | ||
| self.max_seq_len = attention_mha.max_seq_len | ||
| self.head_dim = attention_mha.dim // self.n_heads | ||
| self.SDPA = SDPASHA( | ||
| self.max_batch_size, | ||
| self.max_seq_len, | ||
| self.n_heads, | ||
| self.n_rep, | ||
| self.head_dim, | ||
| self.dim, | ||
| ) | ||
| self.wq = nn.ModuleList( | ||
| [ | ||
| nn.Linear(self.dim, self.head_dim, bias=False) | ||
| for _ in range(self.n_heads) | ||
| ] | ||
| ) | ||
| self.wk = nn.ModuleList( | ||
| [ | ||
| nn.Linear(self.dim, self.head_dim, bias=False) | ||
| for _ in range(self.n_kv_heads) | ||
| ] | ||
| ) | ||
| self.wv = nn.ModuleList( | ||
| [ | ||
| nn.Linear(self.dim, self.head_dim, bias=False) | ||
| for _ in range(self.n_kv_heads) | ||
| ] | ||
| ) | ||
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| for i in range(self.n_heads): | ||
| self.wq[i].weight.data.copy_( | ||
| attention_mha.wq.weight[i * self.head_dim : (i + 1) * self.head_dim] | ||
| ) | ||
| for i in range(self.n_kv_heads): | ||
| self.wk[i].weight.data.copy_( | ||
| attention_mha.wk.weight[i * self.head_dim : (i + 1) * self.head_dim] | ||
| ) | ||
| self.wv[i].weight.data.copy_( | ||
| attention_mha.wv.weight[i * self.head_dim : (i + 1) * self.head_dim] | ||
| ) | ||
| self.wo = attention_mha.wo | ||
|
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| causal_mask = torch.tril( | ||
| torch.ones( | ||
| self.max_seq_len, | ||
| self.max_seq_len, | ||
| dtype=torch.bool, | ||
| device="cpu", | ||
| ) | ||
| ) | ||
| self.register_buffer("mask", causal_mask, persistent=False) | ||
|
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||
| def forward( | ||
| self, | ||
| x: torch.Tensor, | ||
| freqs_cos: torch.Tensor, | ||
| freqs_sin: torch.Tensor, | ||
| input_pos: Optional[torch.Tensor] = None, | ||
| ): | ||
| # QKV | ||
| q = [wq(x) for wq in self.wq] | ||
| k = [wk(x) for wk in self.wk] | ||
| v = [wv(x) for wv in self.wv] | ||
| for i in range(len(q)): | ||
| q[i] = apply_rotary_emb_single(q[i], freqs_cos, freqs_sin) | ||
| for i in range(len(k)): | ||
| k[i] = apply_rotary_emb_single(k[i], freqs_cos, freqs_sin) | ||
|
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| output = self.SDPA(input_pos, q, k, v, self.mask) | ||
| return self.wo(output) | ||
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| def replace_attention_to_attention_sha(module: torch.nn.Module): | ||
| for name, child in module.named_children(): | ||
| if isinstance(child, Attention): | ||
| setattr( | ||
| module, | ||
| name, | ||
| AttentionSHA(child), | ||
| ) | ||
| else: | ||
| replace_attention_to_attention_sha(child) | ||
| return module |