From 248ae7fad20eea2eb1fa706820f85c1659e828ce Mon Sep 17 00:00:00 2001 From: Jiao Wang Date: Fri, 5 Jan 2024 11:30:18 -0800 Subject: [PATCH] LLama optimize_model to support transformers 4.36 (#9818) * supoort 4.36 * style * update * update * update --- .../llm/src/bigdl/llm/transformers/convert.py | 33 ++- .../bigdl/llm/transformers/models/llama.py | 209 +++++++++++++++++- 2 files changed, 228 insertions(+), 14 deletions(-) diff --git a/python/llm/src/bigdl/llm/transformers/convert.py b/python/llm/src/bigdl/llm/transformers/convert.py index 25f97f1f..9aaa8762 100644 --- a/python/llm/src/bigdl/llm/transformers/convert.py +++ b/python/llm/src/bigdl/llm/transformers/convert.py @@ -432,10 +432,6 @@ def _optimize_post(model, lightweight_bmm=False): trans_version = transformers.__version__ if version.parse(trans_version) >= version.parse("4.31.0"): - convert_forward( - model, - transformers.models.llama.modeling_llama.LlamaAttention, - llama_attention_forward_4_31,) convert_forward( model, transformers.models.llama.modeling_llama.LlamaRMSNorm, @@ -443,17 +439,30 @@ def _optimize_post(model, lightweight_bmm=False): convert_forward(model, transformers.models.llama.modeling_llama.LlamaMLP, llama_mlp_forward) - if enable_vllm_se_batching: - convert_forward( - model, - transformers.models.llama.modeling_llama.LlamaModel, - llama_model_selective_batching_forward_4_31, - ) + if version.parse(trans_version) >= version.parse("4.36.0"): + # transformers version >= 4.36.0 + from bigdl.llm.transformers.models.llama import llama_attention_forward_4_36 convert_forward( model, transformers.models.llama.modeling_llama.LlamaAttention, - llama_attention_selective_batching_forward_4_31, - ) + llama_attention_forward_4_36, ) + else: + # transformers version between 4.31.0 - 4.35.2 + convert_forward( + model, + transformers.models.llama.modeling_llama.LlamaAttention, + llama_attention_forward_4_31, ) + if enable_vllm_se_batching: + convert_forward( + model, + transformers.models.llama.modeling_llama.LlamaModel, + llama_model_selective_batching_forward_4_31, + ) + convert_forward( + model, + transformers.models.llama.modeling_llama.LlamaAttention, + llama_attention_selective_batching_forward_4_31, + ) else: # todo implement 4.28.0 ~ 4.30.2 pass diff --git a/python/llm/src/bigdl/llm/transformers/models/llama.py b/python/llm/src/bigdl/llm/transformers/models/llama.py index a4f6dfc0..4b7dc3ad 100644 --- a/python/llm/src/bigdl/llm/transformers/models/llama.py +++ b/python/llm/src/bigdl/llm/transformers/models/llama.py @@ -32,15 +32,16 @@ # limitations under the License. import torch +import warnings import importlib import torch.nn as nn from typing import Optional, Tuple, Union, List import math import os import torch.nn.functional as F -from bigdl.llm.utils.common import invalidInputError from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache -from bigdl.llm.transformers.models.utils import is_enough_kv_cache_room_4_31, apply_rotary_pos_emb +from bigdl.llm.transformers.models.utils import is_enough_kv_cache_room_4_31, \ + apply_rotary_pos_emb, is_enough_kv_cache_room_4_36 from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu from bigdl.llm.transformers.models.utils import use_flash_attention, use_esimd_sdp from transformers.modeling_outputs import BaseModelOutputWithPast @@ -510,6 +511,210 @@ def llama_attention_selective_batching_forward_4_31( return attn_output.to(original_dtype), attn_weights, updated_past_key_values +def llama_attention_forward_4_36( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs +) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. " + "Please make sure use `attention_mask` instead.`" + ) + + bsz, q_len, _ = hidden_states.size() + device = hidden_states.device + # for flash attention + original_dtype = hidden_states.dtype + if not self.training and not hidden_states.requires_grad: + fsdp_flag = use_flash_attention(hidden_states) + else: + fsdp_flag = False + if fsdp_flag: + attention_dtype = torch.float16 # use fp16 for flash attention + else: + attention_dtype = original_dtype + + use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids) + enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx) + qtype = getattr(self.q_proj, "qtype", None) + is_q4_0 = qtype == SYM_INT4 + no_tp = not self.config.pretraining_tp > 1 + decoding_fast_path = (no_tp and is_q4_0 and use_fuse_rope and + enough_kv_room and bsz * q_len == 1) + + # single batch decoding fast path + # forward_qkv takes will perform QKV projection, rotary position embedding + # and save the key/value states to cache, then return query states and the + # extended key/value cache + if decoding_fast_path: + hidden_states = hidden_states.view(1, -1) + cache_k = past_key_value.key_cache[self.layer_idx] + cache_v = past_key_value.value_cache[self.layer_idx] + kv_seq_len = cache_k.shape[-2] + import linear_q4_0 + query_states, key_states, value_states = linear_q4_0.forward_qkv(hidden_states, + self.q_proj.weight, + self.k_proj.weight, + self.v_proj.weight, + position_ids, + cache_k, cache_v, + self.q_proj.weight.qtype, + kv_seq_len, + self.head_dim) + kv_seq_len += 1 + # update past_key_value's seem_tokens and kv caches. + if self.layer_idx == 0: + past_key_value.seen_tokens = kv_seq_len + past_key_value.key_cache[self.layer_idx] = key_states + past_key_value.value_cache[self.layer_idx] = value_states + + else: + if self.config.pretraining_tp > 1: + key_value_slicing = ((self.num_key_value_heads * self.head_dim) // + self.config.pretraining_tp) + query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim) + // self.config.pretraining_tp, dim=0) + key_slices = self.k_proj.weight.split(key_value_slicing, dim=0) + value_slices = self.v_proj.weight.split(key_value_slicing, dim=0) + + query_states = [F.linear(hidden_states, query_slices[i]) + for i in range(self.config.pretraining_tp)] + query_states = torch.cat(query_states, dim=-1) + + key_states = [F.linear(hidden_states, key_slices[i]) + for i in range(self.config.pretraining_tp)] + key_states = torch.cat(key_states, dim=-1) + + value_states = [F.linear(hidden_states, value_slices[i]) + for i in range(self.config.pretraining_tp)] + value_states = torch.cat(value_states, dim=-1) + else: + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + 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) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + invalidInputError(False, + "The cache structure has changed since version v4.36. " + f"If you are using {self.__class__.__name__} for " + "auto-regressive decodingwith k/v caching, please make sure " + "to initialize the attention class with a layer index.") + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + + if use_fuse_rope: + query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states, + key_states, + position_ids, + "llama") + else: + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, + cos, sin, position_ids, "llama") + + if past_key_value is not None: + # update the number of seen tokens + if self.layer_idx == 0: + past_key_value.seen_tokens += key_states.shape[-2] + + # reuse k, v, self_attention + # update `past_key_value` with `key_states` and `value_states` for layer `layer_idx` + if len(past_key_value.key_cache) <= self.layer_idx: + past_key_value.key_cache.append(key_states) + past_key_value.value_cache.append(value_states) + else: + cache_k = past_key_value.key_cache[self.layer_idx] + cache_v = past_key_value.value_cache[self.layer_idx] + + if not enough_kv_room: + # allocate new + new_c_k, new_c_v = extend_kv_cache(bsz, + self.num_key_value_heads, # Support GQA + self.head_dim, + cache_k.size(2), + kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH, + dtype=cache_k.dtype, + device=device) + + new_c_k[:] = cache_k + new_c_v[:] = cache_v + cache_k = new_c_k + cache_v = new_c_v + + key_states, value_states = append_kv_cache(cache_k, + cache_v, + key_states, + value_states) + + # update past_key_value + past_key_value.key_cache[self.layer_idx] = key_states + past_key_value.value_cache[self.layer_idx] = value_states + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups).to(device, + dtype=attention_dtype) + value_states = repeat_kv(value_states, self.num_key_value_groups).to(device, + dtype=attention_dtype) + + if fsdp_flag: + # now only use flash attention for first token + attn_output = F.scaled_dot_product_attention(query_states.to(dtype=attention_dtype), + key_states, + value_states, + is_causal=True) + attn_weights = None + elif use_esimd_sdp(q_len, self.head_dim, query_states): + import linear_fp16_esimd + attn_output = linear_fp16_esimd.sdp_forward(query_states, + key_states.contiguous(), + value_states.contiguous()) + attn_output = attn_output.view(query_states.shape) + attn_weights = None + else: + # otherwise, use native attention + attn_output, attn_weights = native_sdp(query_states, key_states, value_states, + attention_mask, + bsz, q_len, kv_seq_len, + self.head_dim, self.num_heads) + + attn_output_size = (bsz, self.num_heads, q_len, self.head_dim) + if attn_output.size() != attn_output_size: + invalidInputError(False, + f"`attn_output` should be of size {attn_output_size}," + f" but is {attn_output.size()}") + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + if self.config.pretraining_tp > 1: + attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2) + o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, + dim=1) + attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) + for i in range(self.config.pretraining_tp)]) + else: + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output.to(original_dtype), attn_weights, past_key_value + + def native_sdp(query, key, value, attention_mask, bsz, q_len, kv_seq_len, head_dim, num_heads): attn_weights = torch.matmul(query,