1921 lines
88 KiB
Python
1921 lines
88 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Some parts of this file is adapted from
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# https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/llama/modeling_llama.py
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# which is licensed under Apache License 2.0:
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#
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import warnings
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import importlib
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import torch.nn as nn
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from typing import Optional, Tuple, Union, List
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import math
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import os
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import torch.nn.functional as F
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from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
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from ipex_llm.transformers.models.utils import SILU
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from ipex_llm.transformers.models.utils import init_fp8_kv_cache, append_fp8_kv_cache, \
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restore_fp8_kv_cache, use_quantize_kv_cache
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from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_31, \
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apply_rotary_pos_emb, is_enough_kv_cache_room_4_36
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from ipex_llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu
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from ipex_llm.transformers.models.utils import use_flash_attention, use_esimd_sdp
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from ipex_llm.transformers.models.utils import mlp_fusion_check, fp16_fusion_check
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from transformers.modeling_outputs import BaseModelOutputWithPast
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from transformers.models.llama.modeling_llama import LlamaModel
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from ipex_llm.transformers.low_bit_linear import SYM_INT4, FP8E5, IQ2_XXS, FP4
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from ipex_llm.ggml.quantize import ggml_tensor_qtype
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from ipex_llm.utils.common import invalidInputError
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try:
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from transformers.cache_utils import Cache, DynamicCache
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except ImportError:
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Cache = Tuple[torch.Tensor]
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from transformers import logging
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logger = logging.get_logger(__name__)
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def llama_decoding_fast_path_qtype_check(proj):
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# IQ2_XXS only can be used in Llama-like model
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qtype = getattr(proj, "qtype", None)
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return qtype in [SYM_INT4, FP8E5, IQ2_XXS, FP4]
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states
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go from (batch, num_key_value_heads, seqlen, head_dim) to
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(batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads,
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n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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KV_CACHE_ALLOC_BLOCK_LENGTH = os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256)
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_ipex_version = None
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def get_ipex_version():
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global _ipex_version
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if _ipex_version is not None:
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return _ipex_version
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import intel_extension_for_pytorch as ipex
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_ipex_version = ipex.__version__
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return _ipex_version
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def llama_model_forward_4_36(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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) -> Union[Tuple, BaseModelOutputWithPast]:
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from ipex_llm.transformers.kv import DynamicFp8Cache
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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if use_cache and use_quantize_kv_cache(self.layers[0].mlp.up_proj, input_ids):
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if not isinstance(past_key_values, DynamicFp8Cache):
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past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
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return llama_model_forward_4_36_internal(
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self=self,
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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def llama_rms_norm_forward(self, hidden_states):
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if hidden_states.device.type == "xpu" and not (self.training and hidden_states.requires_grad):
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import linear_q4_0
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x_2d = hidden_states.reshape(-1, hidden_states.size(-1)).contiguous()
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output = linear_q4_0.rms_norm(self.weight, x_2d, self.variance_epsilon)
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if 1 < x_2d.size(0) <= 64: # may use XMX, need copy
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output = output.clone()
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return output.reshape(hidden_states.shape)
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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def llama_mlp_forward(
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self,
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x: torch.Tensor,
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residual=None
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) -> torch.Tensor:
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x_2d = x.view(-1, x.shape[-1])
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bsz, hidden_size = x_2d.shape
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qtype = getattr(self.gate_proj, "qtype", None)
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if mlp_fusion_check(x_2d, qtype, self.training) and not self.down_proj.enable_xetla:
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import linear_q4_0
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if not x_2d.is_contiguous():
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x_2d = x_2d.contiguous()
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out = self.down_proj(linear_q4_0.mlp_forward_xpu(
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x_2d, self.gate_proj.weight.data, self.up_proj.weight.data,
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x_2d.shape[0], x_2d.shape[1], self.gate_proj.out_len,
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SILU, qtype
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))
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if residual is not None:
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return out + residual
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else:
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return out
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elif fp16_fusion_check(self.gate_proj, x, self.training) and \
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hidden_size == 4096 and bsz == 1:
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hidden_states1 = torch.ops.torch_ipex.mm_silu(x, self.gate_proj.weight)
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hidden_states = torch.ops.torch_ipex.mm_resmul(
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x, self.up_proj.weight, hidden_states1
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)
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if residual is None:
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hidden_states = torch.matmul(hidden_states, self.down_proj.weight)
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else:
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attn_output = torch.addmm(
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residual.flatten(0, -2),
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hidden_states.flatten(0, -2),
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self.down_proj.weight,
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beta=1,
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)
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hidden_states = attn_output.view(x.shape)
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return hidden_states
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else:
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a = self.act_fn(self.gate_proj(x))
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b = self.up_proj(x)
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c = a * b
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del a, b
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out = self.down_proj(c)
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if residual is not None:
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return out + residual
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else:
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return out
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def should_use_fuse_rope(self, query_states, position_ids):
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use_fuse_rope = query_states.device.type == "xpu"
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use_fuse_rope = use_fuse_rope and not (self.training and query_states.requires_grad)
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use_fuse_rope = use_fuse_rope and self.config.rope_scaling is None
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use_fuse_rope = use_fuse_rope and position_ids is not None
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return use_fuse_rope
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# Only for xpu and training
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def should_use_fast_rope(self, query_states, position_ids):
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use_fuse_rope = query_states.device.type == "xpu"
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use_fuse_rope = use_fuse_rope and (self.training or query_states.requires_grad)
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use_fuse_rope = use_fuse_rope and self.config.rope_scaling is None
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use_fuse_rope = use_fuse_rope and position_ids is not None
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return use_fuse_rope
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def llama_decoder_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.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: Optional[bool] = False,
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use_cache: Optional[bool] = False,
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**kwargs,
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) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
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if "padding_mask" in kwargs:
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warnings.warn(
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"Passing `padding_mask` is deprecated and will be removed in v4.37."
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"Please make sure use `attention_mask` instead.`"
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)
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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# Self Attention
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hidden_states, self_attn_weights, present_key_value = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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**kwargs,
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)
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hidden_states = residual + hidden_states
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# Fully Connected
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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# add residual into mlp
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hidden_states = self.mlp(hidden_states, residual)
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outputs = (hidden_states,)
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if output_attentions:
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outputs += (self_attn_weights,)
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if use_cache:
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outputs += (present_key_value,)
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return outputs
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def fuse_qkv_weight(q_proj, k_proj, v_proj):
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weight_size = q_proj.out_len * q_proj.in_len // 2
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zeros_size = q_proj.in_len * q_proj.out_len // 2 // 64
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zeros_end = weight_size + zeros_size
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weight_byte_shape = (q_proj.in_len//2, q_proj.out_len)
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zeros_byte_shape = (q_proj.in_len//64, q_proj.out_len//2)
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scales_byte_shape = (q_proj.in_len//64, q_proj.out_len*2)
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qweight = torch.concat([q_proj.weight.data[:weight_size].reshape(weight_byte_shape),
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k_proj.weight.data[:weight_size].reshape(weight_byte_shape),
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v_proj.weight.data[:weight_size].reshape(weight_byte_shape),
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], dim=-1).reshape(-1)
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qzeros = torch.concat([q_proj.weight.data[weight_size:zeros_end].reshape(zeros_byte_shape),
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k_proj.weight.data[weight_size:zeros_end].reshape(zeros_byte_shape),
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v_proj.weight.data[weight_size:zeros_end].reshape(zeros_byte_shape),
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], dim=-1).reshape(-1)
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qscales = torch.concat([q_proj.weight.data[zeros_end:].reshape(scales_byte_shape),
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k_proj.weight.data[zeros_end:].reshape(scales_byte_shape),
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v_proj.weight.data[zeros_end:].reshape(scales_byte_shape),
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], dim=-1).reshape(-1)
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q_proj.weight.data = torch.empty(0)
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k_proj.weight.data = torch.empty(0)
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v_proj.weight.data = torch.empty(0)
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return torch.cat([qweight, qzeros, qscales], dim=0)
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def should_use_mm_int4_qkv(self, device):
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return device.type == "xpu" and self.q_proj.qtype == SYM_INT4 and self.q_proj.enable_xetla
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def llama_attention_forward_4_31(
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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.LongTensor] = 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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padding_mask: Optional[torch.LongTensor] = None,
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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if use_quantize_kv_cache(self.q_proj, hidden_states):
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forward_function = llama_attention_forward_4_31_quantized
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else:
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forward_function = llama_attention_forward_4_31_original
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return forward_function(
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self=self,
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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padding_mask=padding_mask,
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kwargs=kwargs
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)
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def llama_attention_forward_4_31_quantized(
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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.LongTensor] = 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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padding_mask: Optional[torch.LongTensor] = None,
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, hidden_size = hidden_states.size()
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device = hidden_states.device
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# for flash attention
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original_dtype = hidden_states.dtype
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use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
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enough_kv_room = is_enough_kv_cache_room_4_31(past_key_value, seq_len=q_len)
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qtype_check = llama_decoding_fast_path_qtype_check(self.q_proj)
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no_tp = not self.config.pretraining_tp > 1
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decoding_fast_path = (no_tp and qtype_check and use_fuse_rope
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and enough_kv_room and bsz * q_len == 1)
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# single batch decoding fast path
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# forward_qkv takes will perform QKV projection, rotary position embedding
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# and save the key/value states to cache, then return query states and the
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# extended key/value cache
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if decoding_fast_path:
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hidden_states = hidden_states.view(1, -1)
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tmp_cache_k, tmp_cache_v = init_kv_cache(
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bsz,
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self.num_key_value_heads,
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self.head_dim,
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0,
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1,
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dtype=hidden_states.dtype,
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device=device
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)
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import linear_q4_0
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query_states, key_states, value_states = linear_q4_0.forward_qkv(hidden_states,
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self.q_proj.weight,
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self.k_proj.weight,
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self.v_proj.weight,
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position_ids,
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tmp_cache_k, tmp_cache_v,
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self.q_proj.weight.qtype,
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self.v_proj.weight.qtype,
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0,
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self.head_dim,
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self.rotary_emb.base,)
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else:
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len,
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self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len,
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self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len,
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self.num_key_value_heads, self.head_dim).transpose(1, 2)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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kv_seq_len += past_key_value[0].shape[-2]
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if use_fuse_rope:
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query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
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key_states,
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position_ids,
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"llama")
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else:
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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(query_states, key_states,
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cos, sin, position_ids, "llama")
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if past_key_value is None:
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kv_seq_len = key_states.shape[-2]
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repeated_key_states = repeat_kv(key_states, self.num_key_value_groups)
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repeated_value_states = repeat_kv(value_states, self.num_key_value_groups)
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attn_output, attn_weights = native_sdp(query_states, repeated_key_states,
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repeated_value_states, attention_mask,
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bsz, q_len, kv_seq_len,
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self.head_dim, self.num_heads)
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if use_cache:
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k_cache, v_cache = init_fp8_kv_cache(
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bsz, self.num_key_value_heads, kv_seq_len, self.head_dim,
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device=query_states.device, new_layout=True
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)
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key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
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key_states, value_states)
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past_key_value = (key_states, value_states)
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else:
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k_cache, v_cache = past_key_value
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key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
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key_states, value_states, new_layout=True)
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kv_seq_len = key_states.shape[-2]
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past_key_value = (key_states, value_states)
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if query_states.size(2) != 1 or query_states.device.type != 'xpu':
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key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
|
|
query_states.dtype)
|
|
# 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)
|
|
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)
|
|
else:
|
|
import linear_q4_0
|
|
attn_output = linear_q4_0.sdp_fp8(query_states, key_states, value_states,
|
|
attention_mask)
|
|
attn_weights = None
|
|
|
|
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 llama_attention_forward_4_31_original(
|
|
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,
|
|
padding_mask: Optional[torch.LongTensor] = None,
|
|
**kwargs,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
bsz, q_len, hidden_size = hidden_states.size()
|
|
device = hidden_states.device
|
|
# for flash attention
|
|
original_dtype = hidden_states.dtype
|
|
|
|
use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
|
|
enough_kv_room = is_enough_kv_cache_room_4_31(past_key_value, seq_len=q_len)
|
|
qtype_check = llama_decoding_fast_path_qtype_check(self.q_proj)
|
|
no_tp = not self.config.pretraining_tp > 1
|
|
decoding_fast_path = (no_tp and qtype_check and use_fuse_rope and
|
|
enough_kv_room and bsz * q_len == 1)
|
|
decoding_fast_path = decoding_fast_path and not self.q_proj.enable_xetla
|
|
|
|
# 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)
|
|
kv_seq_len = past_key_value[0].shape[-2]
|
|
cache_k = past_key_value[0]
|
|
cache_v = past_key_value[1]
|
|
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,
|
|
self.v_proj.weight.qtype,
|
|
kv_seq_len,
|
|
self.head_dim,
|
|
self.rotary_emb.base,)
|
|
kv_seq_len += 1
|
|
|
|
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:
|
|
if fp16_fusion_check(self.q_proj, hidden_states, self.training) and \
|
|
hidden_size == 4096:
|
|
# only use mm_qkv_out on pvc for llama-7b
|
|
if not hasattr(self, "qkv_proj_weight"):
|
|
self.qkv_proj_weight = torch.stack([self.q_proj.weight,
|
|
self.k_proj.weight,
|
|
self.v_proj.weight]).contiguous()
|
|
self.q_proj.weight.data = self.qkv_proj_weight[0, :, :]
|
|
self.k_proj.weight.data = self.qkv_proj_weight[1, :, :]
|
|
self.v_proj.weight.data = self.qkv_proj_weight[2, :, :]
|
|
torch.xpu.empty_cache()
|
|
query_states = torch.empty(bsz, q_len, self.qkv_proj_weight.shape[-1],
|
|
dtype=hidden_states.dtype, device=hidden_states.device)
|
|
key_states = torch.empty(bsz, q_len, self.qkv_proj_weight.shape[-1],
|
|
dtype=hidden_states.dtype, device=hidden_states.device)
|
|
value_states = torch.empty(bsz, q_len, self.qkv_proj_weight.shape[-1],
|
|
dtype=hidden_states.dtype, device=hidden_states.device)
|
|
torch.ops.torch_ipex.mm_qkv_out(
|
|
hidden_states, self.qkv_proj_weight, None,
|
|
query_states, key_states, value_states
|
|
)
|
|
else:
|
|
if should_use_mm_int4_qkv(self, device):
|
|
if not hasattr(self, "qkv_proj_qweight"):
|
|
self.qkv_proj_qweight = fuse_qkv_weight(self.q_proj,
|
|
self.k_proj,
|
|
self.v_proj)
|
|
import linear_q4_0
|
|
qkv_states = linear_q4_0.mm_int4(hidden_states, self.qkv_proj_qweight)
|
|
query_states = qkv_states[:, :, :hidden_size]
|
|
key_states = qkv_states[:, :, hidden_size:2*hidden_size]
|
|
value_states = qkv_states[:, :, 2*hidden_size:]
|
|
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:
|
|
kv_seq_len += past_key_value[0].shape[-2]
|
|
|
|
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:
|
|
# reuse k, v, self_attention
|
|
cache_k = past_key_value[0]
|
|
cache_v = past_key_value[1]
|
|
if not enough_kv_room:
|
|
# allocate new
|
|
new_cache_k, new_cache_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_cache_k[:] = cache_k
|
|
new_cache_v[:] = cache_v
|
|
cache_k = new_cache_k
|
|
cache_v = new_cache_v
|
|
|
|
key_states, value_states = append_kv_cache(cache_k, cache_v, key_states, value_states)
|
|
|
|
elif use_cache:
|
|
max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
|
|
new_key_states, new_value_states = init_kv_cache(bsz,
|
|
self.num_key_value_heads,
|
|
self.head_dim,
|
|
kv_seq_len,
|
|
max_cache_length,
|
|
dtype=key_states.dtype,
|
|
device=device)
|
|
new_key_states[:] = key_states
|
|
new_value_states[:] = value_states
|
|
key_states = new_key_states
|
|
value_states = new_value_states
|
|
|
|
past_key_value = (key_states, value_states) if use_cache else None
|
|
|
|
# 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)
|
|
|
|
if not self.training and not hidden_states.requires_grad and \
|
|
use_flash_attention(query_states, key_states, attention_mask):
|
|
attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
|
|
key_states.to(device, dtype=torch.float16),
|
|
value_states.to(device, dtype=torch.float16),
|
|
is_causal=True)
|
|
attn_weights = None
|
|
elif not self.training and not hidden_states.requires_grad and \
|
|
use_esimd_sdp(q_len, key_states.shape[2], self.head_dim, query_states, attention_mask):
|
|
import linear_fp16_esimd
|
|
attn_output = linear_fp16_esimd.sdp_forward(query_states,
|
|
key_states,
|
|
value_states)
|
|
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 llama_attention_selective_batching_forward_4_31(
|
|
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,
|
|
padding_mask: Optional[torch.LongTensor] = None,
|
|
**kwargs,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
# Minimize this value to reduce memory allocation.
|
|
VLLM_KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get('VLLM_KV_CACHE_ALLOC_BLOCK', 64))
|
|
bsz, q_len, _ = hidden_states.size()
|
|
device = hidden_states.device
|
|
# for flash attention
|
|
original_dtype = hidden_states.dtype
|
|
# TODO: consider this later - flash attention
|
|
# if not self.training and not hidden_states.requires_grad:
|
|
# fsdp_flag = use_flash_attention(hidden_states)
|
|
# else:
|
|
# fsdp_flag = False
|
|
# if fsdp_flag and q_len > 1:
|
|
# attention_dtype = torch.float16 # use fp16 for flash attention
|
|
# else:
|
|
# attention_dtype = original_dtype
|
|
|
|
attention_dtype = original_dtype
|
|
|
|
# TODO: decoding fast path
|
|
use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
|
|
enough_kv_room = past_key_value is not None and is_enough_kv_cache_room_4_31(past_key_value[0])
|
|
qtype_check = llama_decoding_fast_path_qtype_check(self.q_proj)
|
|
no_tp = not self.config.pretraining_tp > 1
|
|
decoding_fast_path = (no_tp and qtype_check and use_fuse_rope and
|
|
bsz * q_len == 1)
|
|
decoding_fast_path = decoding_fast_path and not self.q_proj.enable_xetla
|
|
|
|
updated_past_key_values = []
|
|
# 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:
|
|
past_k = past_key_value[0][0]
|
|
past_v = past_key_value[0][1]
|
|
kv_seq_len = past_k.shape[-2]
|
|
if not enough_kv_room:
|
|
new_cache_k, new_cache_v = extend_kv_cache(1,
|
|
self.num_key_value_heads, # Support GQA
|
|
self.head_dim,
|
|
kv_seq_len,
|
|
kv_seq_len +
|
|
VLLM_KV_CACHE_ALLOC_BLOCK_LENGTH,
|
|
dtype=past_k.dtype,
|
|
device=device)
|
|
new_cache_k[:] = past_k
|
|
new_cache_v[:] = past_v
|
|
past_k = new_cache_k
|
|
past_v = new_cache_v
|
|
hidden_states = hidden_states.view(1, -1)
|
|
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,
|
|
past_k, past_v,
|
|
self.q_proj.weight.qtype,
|
|
self.v_proj.weight.qtype,
|
|
kv_seq_len,
|
|
self.head_dim,
|
|
self.rotary_emb.base,
|
|
)
|
|
kv_seq_len += 1
|
|
else:
|
|
if self.config.pretraining_tp > 1:
|
|
invalidInputError(False, f"vLLM: config.pretraining_tp > 1 not supported yet")
|
|
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:
|
|
kv_seq_len += max(kv_pair[0].shape[-2] for kv_pair in past_key_value)
|
|
|
|
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:
|
|
batched_attention_output = []
|
|
# print(f"type of attention_mask is {type(attention_mask)}")
|
|
for batch in range(bsz):
|
|
enough_kv_room = is_enough_kv_cache_room_4_31(past_key_value[batch])
|
|
past_k, past_v = past_key_value[batch]
|
|
current_kv_len = past_k.shape[-2] + 1
|
|
if not enough_kv_room:
|
|
# allocate new
|
|
new_cache_k, new_cache_v = extend_kv_cache(1,
|
|
self.num_key_value_heads,
|
|
self.head_dim,
|
|
past_k.size(2),
|
|
current_kv_len +
|
|
VLLM_KV_CACHE_ALLOC_BLOCK_LENGTH,
|
|
dtype=past_k.dtype,
|
|
device=device)
|
|
new_cache_k[:] = past_k
|
|
new_cache_v[:] = past_v
|
|
past_k = new_cache_k
|
|
past_v = new_cache_v
|
|
|
|
current_key_states = key_states[batch: batch + 1, :, :, :]
|
|
current_value_states = value_states[batch: batch + 1, :, :, :]
|
|
current_key_states, current_value_states = append_kv_cache(past_k,
|
|
past_v,
|
|
current_key_states,
|
|
current_value_states)
|
|
updated_past_key_values.append((current_key_states, current_value_states))
|
|
|
|
current_key_states = repeat_kv(current_key_states, self.num_key_value_groups)
|
|
current_value_states = repeat_kv(current_value_states, self.num_key_value_groups)
|
|
|
|
current_query_states = query_states[batch: batch + 1, :, :, :]
|
|
attn_output, attn_weights = native_sdp(current_query_states,
|
|
current_key_states,
|
|
current_value_states,
|
|
attention_mask[batch],
|
|
1,
|
|
1,
|
|
current_kv_len,
|
|
self.head_dim,
|
|
self.num_heads)
|
|
if attn_output.size() != (1, self.num_heads, 1, self.head_dim):
|
|
invalidInputError(False,
|
|
f"`attn_output` should be of size "
|
|
f"{(1, self.num_heads, 1, self.head_dim)}, but is"
|
|
f" {attn_output.size()}")
|
|
batched_attention_output.append(attn_output)
|
|
# For loop ends
|
|
# TODO: handle attention_weights later
|
|
attn_output = torch.concat(batched_attention_output, dim=0)
|
|
batched_attention_output.clear()
|
|
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
|
invalidInputError(False,
|
|
f"`attn_output` should be of size "
|
|
f"{(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
|
f" {attn_output.size()}")
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
|
attn_output = self.o_proj(attn_output)
|
|
return attn_output, None, updated_past_key_values
|
|
|
|
# Assume always use_cache
|
|
# prefill or decoding fast path
|
|
for batch in range(bsz):
|
|
updated_past_key_values.append((key_states[batch: batch + 1, :, :, :],
|
|
value_states[batch: batch+1, :, :, :]))
|
|
|
|
# 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)
|
|
# Can also happens for decoding fast path
|
|
if isinstance(attention_mask, list):
|
|
# For decoding fast path
|
|
attention_mask = attention_mask[0]
|
|
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)
|
|
|
|
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
|
invalidInputError(False,
|
|
f"`attn_output` should be of size "
|
|
f"{(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
|
f" {attn_output.size()}")
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
|
|
|
attn_output = self.o_proj(attn_output)
|
|
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[Cache] = None,
|
|
output_attentions: bool = False,
|
|
use_cache: bool = False,
|
|
**kwargs
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
|
|
if use_quantize_kv_cache(self.q_proj, hidden_states):
|
|
forward_function = llama_attention_forward_4_36_quantized
|
|
else:
|
|
forward_function = llama_attention_forward_4_36_original
|
|
return forward_function(
|
|
self=self,
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
kwargs=kwargs
|
|
)
|
|
|
|
|
|
def llama_attention_forward_4_36_quantized(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: bool = False,
|
|
use_cache: bool = False,
|
|
**kwargs
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
|
|
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
|
|
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, seq_len=q_len)
|
|
qtype_check = llama_decoding_fast_path_qtype_check(self.q_proj)
|
|
no_tp = not self.config.pretraining_tp > 1
|
|
decoding_fast_path = (no_tp and qtype_check and use_fuse_rope
|
|
and enough_kv_room and bsz * q_len == 1)
|
|
if decoding_fast_path:
|
|
hidden_states = hidden_states.view(1, -1)
|
|
tmp_cache_k, tmp_cache_v = init_kv_cache(
|
|
bsz,
|
|
self.num_key_value_heads,
|
|
self.head_dim,
|
|
0,
|
|
1,
|
|
dtype=hidden_states.dtype,
|
|
device=device
|
|
)
|
|
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,
|
|
tmp_cache_k, tmp_cache_v,
|
|
self.q_proj.weight.qtype,
|
|
self.v_proj.weight.qtype,
|
|
0,
|
|
self.head_dim,
|
|
self.rotary_emb.base,)
|
|
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,
|
|
f"The cache structure has changed since version v4.36."
|
|
f" If you are using {self.__class__.__name__} "
|
|
f"for auto-regressive decoding with k/v caching,"
|
|
f" 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")
|
|
kv_seq_len = key_states.shape[-2]
|
|
|
|
if len(past_key_value.key_cache) <= self.layer_idx:
|
|
attn_weights = torch.matmul(query_states,
|
|
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
|
|
|
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
|
invalidInputError(
|
|
False,
|
|
f"Attention weights should be of size "
|
|
f"{(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
|
f" {attn_weights.size()}"
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
|
invalidInputError(
|
|
False,
|
|
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
|
|
f" but is {attention_mask.size()}"
|
|
)
|
|
attn_weights = attn_weights + attention_mask
|
|
|
|
# at inference time, for memory considerations, may not need to upcast attention to fp32
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
if use_cache:
|
|
cache_kwargs = None
|
|
key_states, value_states = past_key_value.update(key_states, value_states,
|
|
self.layer_idx, cache_kwargs,
|
|
new_layout=True)
|
|
else:
|
|
cache_kwargs = None # Specific to RoPE models
|
|
key_states, value_states = past_key_value.update(key_states, value_states,
|
|
self.layer_idx, cache_kwargs,
|
|
new_layout=True)
|
|
kv_seq_len = key_states.shape[-2]
|
|
if query_states.size(2) != 1 or query_states.device.type != 'xpu':
|
|
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
|
|
query_states.dtype)
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups)\
|
|
.to(device, dtype=query_states.dtype)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups)\
|
|
.to(device, dtype=query_states.dtype)
|
|
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))
|
|
attn_weights = attn_weights / math.sqrt(self.head_dim)
|
|
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
|
invalidInputError(
|
|
False,
|
|
f"Attention weights should be of size"
|
|
f" {(bsz, self.num_heads, q_len, kv_seq_len)},"
|
|
f" but is {attn_weights.size()}"
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
|
invalidInputError(
|
|
False,
|
|
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
|
|
f" but is {attention_mask.size()}"
|
|
)
|
|
attn_weights = attn_weights + attention_mask
|
|
|
|
# at inference time, for memory considerations, may not need to upcast attention to fp32
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
else:
|
|
import linear_q4_0
|
|
attn_output = linear_q4_0.sdp_fp8(query_states, key_states, value_states,
|
|
attention_mask)
|
|
attn_weights = None
|
|
|
|
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
|
invalidInputError(
|
|
False,
|
|
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)},"
|
|
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, attn_weights, past_key_value
|
|
|
|
|
|
def llama_attention_forward_4_36_original(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: bool = False,
|
|
use_cache: bool = False,
|
|
**kwargs
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
|
|
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_size = hidden_states.size()
|
|
device = hidden_states.device
|
|
# for flash attention
|
|
original_dtype = hidden_states.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, seq_len=q_len)
|
|
qtype_check = llama_decoding_fast_path_qtype_check(self.q_proj)
|
|
no_tp = not self.config.pretraining_tp > 1
|
|
decoding_fast_path = (no_tp and qtype_check and use_fuse_rope and
|
|
enough_kv_room and bsz * q_len == 1)
|
|
decoding_fast_path = decoding_fast_path and not self.q_proj.enable_xetla
|
|
|
|
# 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,
|
|
self.v_proj.weight.qtype,
|
|
kv_seq_len,
|
|
self.head_dim,
|
|
self.rotary_emb.base,)
|
|
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:
|
|
if fp16_fusion_check(self.q_proj, hidden_states, self.training) and \
|
|
hidden_size == 4096:
|
|
# only use mm_qkv_out on pvc for llama-7b
|
|
if not hasattr(self, "qkv_proj_weight"):
|
|
self.qkv_proj_weight = torch.stack([self.q_proj.weight,
|
|
self.k_proj.weight,
|
|
self.v_proj.weight]).contiguous()
|
|
self.q_proj.weight.data = self.qkv_proj_weight[0, :, :]
|
|
self.k_proj.weight.data = self.qkv_proj_weight[1, :, :]
|
|
self.v_proj.weight.data = self.qkv_proj_weight[2, :, :]
|
|
torch.xpu.empty_cache()
|
|
query_states = torch.empty(bsz, q_len, self.qkv_proj_weight.shape[-1],
|
|
dtype=hidden_states.dtype, device=hidden_states.device)
|
|
key_states = torch.empty(bsz, q_len, self.qkv_proj_weight.shape[-1],
|
|
dtype=hidden_states.dtype, device=hidden_states.device)
|
|
value_states = torch.empty(bsz, q_len, self.qkv_proj_weight.shape[-1],
|
|
dtype=hidden_states.dtype, device=hidden_states.device)
|
|
torch.ops.torch_ipex.mm_qkv_out(
|
|
hidden_states, self.qkv_proj_weight, None,
|
|
query_states, key_states, value_states
|
|
)
|
|
else:
|
|
if should_use_mm_int4_qkv(self, device):
|
|
if not hasattr(self, "qkv_proj_qweight"):
|
|
self.qkv_proj_qweight = fuse_qkv_weight(self.q_proj,
|
|
self.k_proj,
|
|
self.v_proj)
|
|
import linear_q4_0
|
|
qkv_states = linear_q4_0.mm_int4(hidden_states, self.qkv_proj_qweight)
|
|
query_states = qkv_states[:, :, :hidden_size]
|
|
key_states = qkv_states[:, :, hidden_size:2*hidden_size]
|
|
value_states = qkv_states[:, :, 2*hidden_size:]
|
|
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)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
|
|
|
if not self.training and not hidden_states.requires_grad and \
|
|
use_flash_attention(query_states, key_states, attention_mask):
|
|
# now only use flash attention for first token
|
|
attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
|
|
key_states.to(device, dtype=torch.float16),
|
|
value_states.to(device, dtype=torch.float16),
|
|
is_causal=True)
|
|
attn_weights = None
|
|
elif not self.training and not hidden_states.requires_grad and \
|
|
use_esimd_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
|
|
import linear_fp16_esimd
|
|
attn_output = linear_fp16_esimd.sdp_forward(query_states,
|
|
key_states,
|
|
value_states)
|
|
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.to(key.dtype),
|
|
key.transpose(2, 3)) / math.sqrt(head_dim)
|
|
|
|
attn_weights_size = (bsz, num_heads, q_len, kv_seq_len)
|
|
if attn_weights.size() != attn_weights_size:
|
|
invalidInputError(False,
|
|
f"Attention weights should be of size {attn_weights_size}, "
|
|
f"but is {attn_weights.size()}")
|
|
|
|
if attention_mask is not None:
|
|
attn_mask_size = (bsz, 1, q_len, kv_seq_len)
|
|
if attention_mask.size() != attn_mask_size:
|
|
invalidInputError(False,
|
|
f"Attention mask should be of size {attn_mask_size}, "
|
|
f"but is {attention_mask.size()}")
|
|
attn_weights = attn_weights + attention_mask
|
|
|
|
# at inference time, for memory considerations, may not need to upcast attention to fp32
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
|
|
attn_output = torch.matmul(attn_weights, value)
|
|
return attn_output, attn_weights
|
|
|
|
|
|
def llama_model_selective_batching_forward_4_31(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPast]:
|
|
if output_attentions is not None:
|
|
output_attentions = output_attentions
|
|
else:
|
|
output_attentions = self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None
|
|
else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# retrieve input_ids and inputs_embeds
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
invalidInputError(False,
|
|
"You cannot specify both decoder_input_ids"
|
|
" and decoder_inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
batch_size, seq_length = input_ids.shape
|
|
elif inputs_embeds is not None:
|
|
batch_size, seq_length, _ = inputs_embeds.shape
|
|
else:
|
|
invalidInputError(False,
|
|
"You have to specify either "
|
|
"decoder_input_ids or decoder_inputs_embeds")
|
|
|
|
# seq_length_with_past = seq_length
|
|
past_key_values_length = 0
|
|
|
|
# The original position_ids in the format of [1, 1]
|
|
# However, this only applies when kv_len is the same for all the sequences
|
|
# We should set it to format of [batch, position_id]
|
|
# TODO: validate correctness
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
if position_ids is None:
|
|
invalidInputError(False,
|
|
"vLLM: position_ids should never be None")
|
|
else:
|
|
# print(f"Original position_ids is {position_ids}")
|
|
position_ids = position_ids.view(-1, seq_length)
|
|
# print(f"after position_ids is {position_ids}")
|
|
# if past_key_values is None:
|
|
# # For prefill
|
|
# position_ids = torch.arange(0, seq_length, dtype=torch.long, device=device)
|
|
# position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
|
# else:
|
|
# past_key_values_length = []
|
|
# for sequence_kv in past_key_values[0]:
|
|
# key = sequence_kv[0]
|
|
# past_key_values_length.append(key.shape[-2])
|
|
# position_ids = torch.tensor(past_key_values_length, dtype=torch.long, device=device)
|
|
# position_ids = position_ids.unsqueeze(0).view(-1, 1)
|
|
|
|
if past_key_values is not None:
|
|
# past_key_values in the format of num_layers x num_seqs x 2
|
|
# TODO: this may be incorrect
|
|
past_key_values_length = past_key_values[0][0][0].shape[2]
|
|
# seq_length_with_past = seq_length_with_past + past_key_values_length
|
|
|
|
# if position_ids is None:
|
|
# device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
# # [start, end)
|
|
# position_ids = torch.arange(
|
|
# past_key_values_length, seq_length +
|
|
# past_key_values_length, dtype=torch.long, device=device
|
|
# )
|
|
# position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
|
# else:
|
|
# position_ids = position_ids.view(-1, seq_length).long()
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
# embed positions
|
|
if attention_mask is None:
|
|
invalidInputError(False, "attention_mask should never be None")
|
|
# print(f"attention_mask before expanding: {attention_mask}")
|
|
if past_key_values is None:
|
|
attention_mask = self._prepare_decoder_attention_mask(
|
|
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
|
)
|
|
else:
|
|
i = 0
|
|
for attn_mask in attention_mask:
|
|
past_key_value_length = past_key_values[0][i][0].shape[2]
|
|
new_mask = self._prepare_decoder_attention_mask(
|
|
attn_mask, (1, seq_length), inputs_embeds, past_key_value_length
|
|
)
|
|
attention_mask[i] = new_mask
|
|
i += 1
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
invalidInputError(False, "gradient_checkpointing is not supported")
|
|
|
|
# decoder layers
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
next_decoder_cache = () if use_cache else None
|
|
|
|
for idx, decoder_layer in enumerate(self.layers):
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
# None for past_key_value
|
|
return module(*inputs, output_attentions, None)
|
|
|
|
return custom_forward
|
|
|
|
layer_outputs = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(decoder_layer),
|
|
hidden_states,
|
|
attention_mask,
|
|
position_ids,
|
|
None,
|
|
)
|
|
else:
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if use_cache:
|
|
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
# add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
next_cache = next_decoder_cache if use_cache else None
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) # noqa
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
|
|
# For training
|
|
def llama_attention_fast_forward(
|
|
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,
|
|
padding_mask: Optional[torch.LongTensor] = None,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
bsz, q_len, _ = hidden_states.size()
|
|
device = hidden_states.device
|
|
use_fast_rope = should_use_fast_rope(self, hidden_states, position_ids)
|
|
|
|
# Check for inference
|
|
if use_cache and past_key_value is not None and q_len == 1:
|
|
A, past_key_value = llama_attention_forward_4_31(
|
|
self,
|
|
hidden_states,
|
|
past_key_value,
|
|
position_ids,
|
|
)
|
|
return A, None, past_key_value
|
|
|
|
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:
|
|
kv_seq_len += past_key_value[0].shape[-2]
|
|
|
|
if use_fast_rope:
|
|
from ipex_llm.transformers.layers.rope_embedding import apply_fast_rope_embedding
|
|
query_states, key_states = apply_fast_rope_embedding(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:
|
|
# 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)
|
|
|
|
past_key_value = (key_states, value_states) if use_cache else None
|
|
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
|
|
|
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, attn_weights, past_key_value
|
|
|
|
|
|
def llama_model_forward_4_36_internal(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPast]:
|
|
output_attentions = output_attentions if output_attentions is not None else \
|
|
self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else
|
|
self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# retrieve input_ids and inputs_embeds
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
invalidInputError(False,
|
|
"You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
batch_size, seq_length = input_ids.shape[:2]
|
|
elif inputs_embeds is not None:
|
|
batch_size, seq_length = inputs_embeds.shape[:2]
|
|
else:
|
|
invalidInputError(False, "You have to specify either input_ids or inputs_embeds")
|
|
|
|
past_key_values_length = 0
|
|
if use_cache:
|
|
use_legacy_cache = not isinstance(past_key_values, Cache)
|
|
if use_legacy_cache:
|
|
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
|
past_key_values_length = past_key_values.get_usable_length(seq_length)
|
|
|
|
if position_ids is None:
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
position_ids = torch.arange(
|
|
past_key_values_length, seq_length + past_key_values_length,
|
|
dtype=torch.long, device=device
|
|
)
|
|
position_ids = position_ids.unsqueeze(0)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
if self._use_flash_attention_2:
|
|
# 2d mask is passed through the layers
|
|
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) \
|
|
else None
|
|
elif self._use_sdpa and not output_attentions:
|
|
# output_attentions=True can not be supported when using SDPA, and we fall back on
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|
# the manual implementation that requires a 4D causal mask in all cases.
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|
from transformers.models.llama.modeling_llama import \
|
|
_prepare_4d_causal_attention_mask_for_sdpa
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|
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
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|
attention_mask,
|
|
(batch_size, seq_length),
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|
inputs_embeds,
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|
past_key_values_length,
|
|
)
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|
else:
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|
# 4d mask is passed through the layers
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|
from transformers.models.llama.modeling_llama import _prepare_4d_causal_attention_mask
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|
attention_mask = _prepare_4d_causal_attention_mask(
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|
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
|
)
|
|
|
|
# embed positions
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|
hidden_states = inputs_embeds
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing."
|
|
" Setting `use_cache=False`..."
|
|
)
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|
use_cache = False
|
|
|
|
# decoder layers
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|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
next_decoder_cache = None
|
|
|
|
for decoder_layer in self.layers:
|
|
if output_hidden_states:
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|
all_hidden_states += (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
decoder_layer.__call__,
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|
hidden_states,
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|
attention_mask,
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|
position_ids,
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|
past_key_values,
|
|
output_attentions,
|
|
use_cache,
|
|
)
|
|
else:
|
|
# bigdl-llm changes:
|
|
curr_device = decoder_layer.input_layernorm.weight.device
|
|
if attention_mask is not None:
|
|
attention_mask = attention_mask.to(curr_device)
|
|
if position_ids is not None:
|
|
position_ids = position_ids.to(curr_device)
|
|
# bigdl-llm changes end
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if use_cache:
|
|
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
# add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
next_cache = None
|
|
if use_cache:
|
|
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache \
|
|
else next_decoder_cache
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, next_cache,
|
|
all_hidden_states, all_self_attns] if v is not None)
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
|
|
def llama_model_forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPast]:
|
|
output_attentions = output_attentions if output_attentions is not None \
|
|
else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else
|
|
self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# retrieve input_ids and inputs_embeds
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
invalidInputError(False,
|
|
"You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
batch_size, seq_length = input_ids.shape
|
|
elif inputs_embeds is not None:
|
|
batch_size, seq_length, _ = inputs_embeds.shape
|
|
else:
|
|
invalidInputError(False, "You have to specify either input_ids or inputs_embeds")
|
|
|
|
seq_length_with_past = seq_length
|
|
past_key_values_length = 0
|
|
|
|
if past_key_values is not None:
|
|
past_key_values_length = past_key_values[0][0].shape[2]
|
|
seq_length_with_past = seq_length_with_past + past_key_values_length
|
|
|
|
if position_ids is None:
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
position_ids = torch.arange(
|
|
past_key_values_length, seq_length + past_key_values_length,
|
|
dtype=torch.long, device=device
|
|
)
|
|
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
|
else:
|
|
position_ids = position_ids.view(-1, seq_length).long()
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
# embed positions
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(
|
|
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
|
|
)
|
|
padding_mask = None
|
|
else:
|
|
if 0 in attention_mask:
|
|
padding_mask = attention_mask
|
|
else:
|
|
padding_mask = None
|
|
|
|
attention_mask = self._prepare_decoder_attention_mask(
|
|
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
|
)
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing."
|
|
" Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
# decoder layers
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
next_decoder_cache = () if use_cache else None
|
|
|
|
for idx, decoder_layer in enumerate(self.layers):
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
# None for past_key_value
|
|
return module(*inputs, past_key_value, output_attentions,
|
|
padding_mask=padding_mask)
|
|
|
|
return custom_forward
|
|
|
|
layer_outputs = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(decoder_layer), hidden_states, attention_mask, position_ids
|
|
)
|
|
else:
|
|
# bigdl-llm changes:
|
|
#
|
|
# Avoid moving `attention_mask`` and `position_ids`` to other devices multiple times.
|
|
#
|
|
# When the model is partitioned on two different devices using
|
|
# `accelerate`'s `dispatch``, a hook to move inputs to the correct device is
|
|
# added to each layer's `forward``, which will result in moving `attention_mask`
|
|
# and `position_ids`, which allocated on device:0, to other devices for each
|
|
# decoder layer not in device:0.
|
|
#
|
|
# To avoid this, we move `attention_mask` and `position_ids` to the device of
|
|
# the current layer before the forward call, so that the moving is only done once
|
|
# for each devices other than devie:0.
|
|
#
|
|
curr_device = decoder_layer.input_layernorm.weight.device
|
|
if attention_mask is not None:
|
|
attention_mask = attention_mask.to(curr_device)
|
|
if position_ids is not None:
|
|
position_ids = position_ids.to(curr_device)
|
|
# bigdl-llm changes end
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
padding_mask=padding_mask,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if use_cache:
|
|
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
# add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
next_cache = next_decoder_cache if use_cache else None
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, next_cache,
|
|
all_hidden_states, all_self_attns] if v is not None)
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|