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Commit 476b1d8d authored by Azure's avatar Azure
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support deepseekv3; runable but have precition problem

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Showing with 2178 additions and 24 deletions
ktransformers/local_chat.py
View file @ 476b1d8d
...@@ -15,6 +15,7 @@ from ktransformers.server.args import ArgumentParser ...@@ -15,6 +15,7 @@ from ktransformers.server.args import ArgumentParser
from ktransformers.models.modeling_deepseek import DeepseekV2ForCausalLM from ktransformers.models.modeling_deepseek import DeepseekV2ForCausalLM
from ktransformers.models.modeling_deepseekv3 import DeepseekV3ForCausalLM
from ktransformers.models.modeling_qwen2_moe import Qwen2MoeForCausalLM from ktransformers.models.modeling_qwen2_moe import Qwen2MoeForCausalLM
from ktransformers.models.modeling_llama import LlamaForCausalLM from ktransformers.models.modeling_llama import LlamaForCausalLM
from ktransformers.models.modeling_mixtral import MixtralForCausalLM from ktransformers.models.modeling_mixtral import MixtralForCausalLM
...@@ -22,6 +23,7 @@ from ktransformers.server.config.config import Config ...@@ -22,6 +23,7 @@ from ktransformers.server.config.config import Config
custom_models = { custom_models = {
"DeepseekV2ForCausalLM": DeepseekV2ForCausalLM, "DeepseekV2ForCausalLM": DeepseekV2ForCausalLM,
"DeepseekV3ForCausalLM": DeepseekV3ForCausalLM,
"Qwen2MoeForCausalLM": Qwen2MoeForCausalLM, "Qwen2MoeForCausalLM": Qwen2MoeForCausalLM,
"LlamaForCausalLM": LlamaForCausalLM, "LlamaForCausalLM": LlamaForCausalLM,
"MixtralForCausalLM": MixtralForCausalLM, "MixtralForCausalLM": MixtralForCausalLM,
...@@ -30,6 +32,8 @@ custom_models = { ...@@ -30,6 +32,8 @@ custom_models = {
ktransformer_rules_dir = os.path.dirname(os.path.abspath(__file__)) + "/optimize/optimize_rules/" ktransformer_rules_dir = os.path.dirname(os.path.abspath(__file__)) + "/optimize/optimize_rules/"
default_optimize_rules = { default_optimize_rules = {
"DeepseekV2ForCausalLM": ktransformer_rules_dir + "DeepSeek-V2-Chat.yaml", "DeepseekV2ForCausalLM": ktransformer_rules_dir + "DeepSeek-V2-Chat.yaml",
# "DeepseekV3ForCausalLM": ktransformer_rules_dir + "DeepSeek-V2-Chat.yaml",
"DeepseekV3ForCausalLM": ktransformer_rules_dir + "DeepSeek-V3-Chat-multi-gpu.yaml",
"Qwen2MoeForCausalLM": ktransformer_rules_dir + "Qwen2-57B-A14B-Instruct.yaml", "Qwen2MoeForCausalLM": ktransformer_rules_dir + "Qwen2-57B-A14B-Instruct.yaml",
"LlamaForCausalLM": ktransformer_rules_dir + "Internlm2_5-7b-Chat-1m.yaml", "LlamaForCausalLM": ktransformer_rules_dir + "Internlm2_5-7b-Chat-1m.yaml",
"MixtralForCausalLM": ktransformer_rules_dir + "Mixtral.yaml", "MixtralForCausalLM": ktransformer_rules_dir + "Mixtral.yaml",
...@@ -74,8 +78,8 @@ def local_chat(): ...@@ -74,8 +78,8 @@ def local_chat():
else: else:
content += line + "\n" content += line + "\n"
if content == "": if content == "":
if config.prompt_file == None or config.prompt_file == "": if True: # config.prompt_file == None or config.prompt_file == "":
content = "Please write a piece of quicksort code in C++." content = "hi"
else: else:
content = open(config.prompt_file, "r").read() content = open(config.prompt_file, "r").read()
elif os.path.isfile(content): elif os.path.isfile(content):
......
ktransformers/models/configuration_deepseekv3.py 0 → 100644
View file @ 476b1d8d
# coding=utf-8
# Copyright 2025 bzantium and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on the DeepSeekV3 implementations from the DeepSeek AI team. (https://huggingface.co/deepseek-ai/DeepSeek-V3)
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" DeepSeekV3 model configuration """
from transformers.configuration_utils import PretrainedConfig
DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class DeepseekV3Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the DeepSeek-V3.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 129280):
Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`DeepseekV3Model`]
hidden_size (`int`, *optional*, defaults to 7168):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 18432):
Dimension of the MLP representations.
moe_intermediate_size (`int`, *optional*, defaults to 2048):
Dimension of the MoE representations.
num_hidden_layers (`int`, *optional*, defaults to 61):
Number of hidden layers in the Transformer decoder.
num_nextn_predict_layers (`int`, *optional*, defaults to 1):
Number of nextn predict layers in the DeepSeekV3 Model.
num_attention_heads (`int`, *optional*, defaults to 128):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*, defaults to 128):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
n_shared_experts (`int`, *optional*, defaults to 1):
Number of shared experts, None means dense model.
n_routed_experts (`int`, *optional*, defaults to 256):
Number of routed experts, None means dense model.
ep_size (`<fill_type>`, *optional*, defaults to 1): <fill_docstring>
routed_scaling_factor (`float`, *optional*, defaults to 2.5):
Scaling factor or routed experts.
kv_lora_rank (`<fill_type>`, *optional*, defaults to 512): <fill_docstring>
q_lora_rank (`<fill_type>`, *optional*, defaults to 1536): <fill_docstring>
qk_rope_head_dim (`<fill_type>`, *optional*, defaults to 64): <fill_docstring>
v_head_dim (`<fill_type>`, *optional*, defaults to 128): <fill_docstring>
qk_nope_head_dim (`<fill_type>`, *optional*, defaults to 128): <fill_docstring>
topk_method (`str`, *optional*, defaults to `"noaux_tc"`):
Topk method used in routed gate.
n_group (`int`, *optional*, defaults to 8):
Number of groups for routed experts.
topk_group (`int`, *optional*, defaults to 4):
Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
num_experts_per_tok (`int`, *optional*, defaults to 8):
Number of selected experts, None means dense model.
moe_layer_freq (`int`, *optional*, defaults to 1):
The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
first_k_dense_replace (`int`, *optional*, defaults to 3):
Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
\--k dense layers--/
norm_topk_prob (`bool`, *optional*, defaults to `True`):
Whether to normalize the weights of the routed experts.
scoring_func (`str`, *optional*, defaults to `"sigmoid"`):
Method of computing expert weights.
aux_loss_alpha (`float`, *optional*, defaults to 0.001):
Auxiliary loss weight coefficient.
Whether to compute the auxiliary loss for each individual sample.
seq_aux (`<fill_type>`, *optional*, defaults to `True`): <fill_docstring>
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 0):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 1):
End of stream token id.
pretraining_tp (`int`, *optional*, defaults to 1):
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
issue](https://github.com/pytorch/pytorch/issues/76232).
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum.
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import DeepseekV3Model, DeepseekV3Config
>>> # Initializing a Deepseek-V3 style configuration
>>> configuration = DeepseekV3Config()
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "deepseek_v3"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=129280,
hidden_size=7168,
intermediate_size=18432,
moe_intermediate_size = 2048,
num_hidden_layers=61,
num_nextn_predict_layers=1,
num_attention_heads=128,
num_key_value_heads=128,
n_shared_experts = 1,
n_routed_experts = 256,
ep_size = 1,
routed_scaling_factor = 2.5,
kv_lora_rank = 512,
q_lora_rank = 1536,
qk_rope_head_dim = 64,
v_head_dim = 128,
qk_nope_head_dim = 128,
topk_method = 'noaux_tc',
n_group = 8,
topk_group = 4,
num_experts_per_tok = 8,
moe_layer_freq = 1,
first_k_dense_replace = 3,
norm_topk_prob = True,
scoring_func = 'sigmoid',
aux_loss_alpha = 0.001,
seq_aux = True,
hidden_act="silu",
max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=0,
eos_token_id=1,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
mlp_bias=False,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.moe_intermediate_size = moe_intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_nextn_predict_layers = num_nextn_predict_layers
self.num_attention_heads = num_attention_heads
self.n_shared_experts = n_shared_experts
self.n_routed_experts = n_routed_experts
self.ep_size = ep_size
self.routed_scaling_factor = routed_scaling_factor
self.kv_lora_rank = kv_lora_rank
self.q_lora_rank = q_lora_rank
self.qk_rope_head_dim = qk_rope_head_dim
self.v_head_dim = v_head_dim
self.qk_nope_head_dim = qk_nope_head_dim
self.topk_method = topk_method
self.n_group = n_group
self.topk_group = topk_group
self.num_experts_per_tok = num_experts_per_tok
self.moe_layer_freq = moe_layer_freq
self.first_k_dense_replace = first_k_dense_replace
self.norm_topk_prob = norm_topk_prob
self.scoring_func = scoring_func
self.aux_loss_alpha = aux_loss_alpha
self.seq_aux = seq_aux
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.mlp_bias = mlp_bias
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
__all__ = ["DeepseekV3Config"]
\ No newline at end of file
ktransformers/models/custom_cache.py
View file @ 476b1d8d
...@@ -34,9 +34,12 @@ class StaticCache(transformers.StaticCache): ...@@ -34,9 +34,12 @@ class StaticCache(transformers.StaticCache):
self.max_batch_size = max_batch_size self.max_batch_size = max_batch_size
self.max_cache_len = config.max_position_embeddings if max_cache_len is None else max_cache_len self.max_cache_len = config.max_position_embeddings if max_cache_len is None else max_cache_len
# Some model define a custom `head_dim` != config.hidden_size // config.num_attention_heads # Some model define a custom `head_dim` != config.hidden_size // config.num_attention_heads
self.head_dim = ( if config.architectures[0] == "DeepseekV3ForCausalLM":
config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads self.head_dim = config.qk_rope_head_dim
) else:
self.head_dim = (
config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads
)
self.dtype = dtype if dtype is not None else torch.float32 self.dtype = dtype if dtype is not None else torch.float32
self.num_key_value_heads = ( self.num_key_value_heads = (
...@@ -46,7 +49,7 @@ class StaticCache(transformers.StaticCache): ...@@ -46,7 +49,7 @@ class StaticCache(transformers.StaticCache):
self.key_cache: List[torch.Tensor] = [] self.key_cache: List[torch.Tensor] = []
self.value_cache: List[torch.Tensor] = [] self.value_cache: List[torch.Tensor] = []
cache_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, self.head_dim) cache_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, self.head_dim)
if config.architectures[0] == "DeepseekV2ForCausalLM": if config.architectures[0] == "DeepseekV2ForCausalLM" or config.architectures[0] == "DeepseekV3ForCausalLM":
# TODO: for deepseek, cache_shape is different whether using Absorbed MLA, check it automatically # TODO: for deepseek, cache_shape is different whether using Absorbed MLA, check it automatically
# key_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, config.qk_rope_head_dim + config.qk_nope_head_dim) # key_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, config.qk_rope_head_dim + config.qk_nope_head_dim)
# value_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, config.v_head_dim) # value_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, config.v_head_dim)
......
ktransformers/models/modeling_deepseekv3.py 0 → 100644
View file @ 476b1d8d
This diff is collapsed.
ktransformers/operators/attention.py
View file @ 476b1d8d
...@@ -13,6 +13,7 @@ from ktransformers.models.configuration_deepseek import DeepseekV2Config ...@@ -13,6 +13,7 @@ from ktransformers.models.configuration_deepseek import DeepseekV2Config
from ktransformers.models.configuration_llama import LlamaConfig from ktransformers.models.configuration_llama import LlamaConfig
from ktransformers.models.modeling_llama import LlamaRotaryEmbedding from ktransformers.models.modeling_llama import LlamaRotaryEmbedding
from ktransformers.models.modeling_deepseek import DeepseekV2Attention, apply_rotary_pos_emb from ktransformers.models.modeling_deepseek import DeepseekV2Attention, apply_rotary_pos_emb
from ktransformers.models.modeling_deepseekv3 import DeepseekV3Attention, apply_rotary_pos_emb
from typing import Optional, Tuple from typing import Optional, Tuple
from ktransformers.operators.base_operator import BaseInjectedModule from ktransformers.operators.base_operator import BaseInjectedModule
from ktransformers.util.custom_gguf import GGUFLoader from ktransformers.util.custom_gguf import GGUFLoader
...@@ -20,6 +21,206 @@ import logging ...@@ -20,6 +21,206 @@ import logging
from transformers.configuration_utils import PretrainedConfig from transformers.configuration_utils import PretrainedConfig
from transformers.cache_utils import Cache from transformers.cache_utils import Cache
logger = logging.getLogger("attention") logger = logging.getLogger("attention")
class KDeepseekV3Attention(BaseInjectedModule, DeepseekV3Attention):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
attn_mask: Optional[torch.Tensor] = None
def __init__(self,
key: str,
gguf_loader : GGUFLoader,
config: PretrainedConfig,
orig_module: nn.Module,
device: str = "cuda",
chunck_size: int = 1000,
**kwargs):
BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs)
self.orig_module.__init__(orig_module.config,
orig_module.layer_idx)
self.chunck_size = chunck_size # TODO, generate chunck_size automatically.
self.softmax_scale = self.q_head_dim ** (-0.5)
def get_absorbed(self) -> Tuple[torch.Tensor, torch.Tensor]:
if not (hasattr(self, 'q_absorb') and hasattr(self, 'out_absorb')):
kv_b_proj = self.kv_b_proj.weight.view(self.num_heads, -1, self.kv_lora_rank)
q_absorb = kv_b_proj[:, :self.qk_nope_head_dim, :].reshape(-1, self.kv_lora_rank)
out_absorb = kv_b_proj[:, self.qk_nope_head_dim:, :].reshape(-1, self.kv_lora_rank)
self.q_absorb = nn.Linear(self.kv_lora_rank, self.num_heads * self.qk_nope_head_dim,
bias=False, dtype=q_absorb.dtype, device=q_absorb.device)
self.q_absorb.weight.data = q_absorb
self.out_absorb = nn.Linear(self.kv_lora_rank, self.num_heads * self.v_head_dim,
bias=False, dtype=out_absorb.dtype, device=out_absorb.device)
self.out_absorb.weight.data = out_absorb
del self.orig_module.kv_b_proj
q_absorb = self.q_absorb.weight.view(self.num_heads, self.qk_nope_head_dim, self.kv_lora_rank)
out_absorb = self.out_absorb.weight.view(self.num_heads, self.v_head_dim, self.kv_lora_rank)
return q_absorb, out_absorb
def forward_chunck(
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,
cache_position: Optional[torch.LongTensor] = None,
**kwargs
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
if self.q_lora_rank is None:
q = self.q_proj(hidden_states)
else:
q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
q_nope, q_pe = torch.split(
q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
)
compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
compressed_kv, k_pe = torch.split(
compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
)
compressed_kv = self.kv_a_layernorm(compressed_kv)
k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
kv_seq_len = k_pe.shape[-2]
if past_key_value is not None:
if self.layer_idx is None:
raise ValueError(
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
"for auto-regressive decoding with 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)
cos, sin = self.rotary_emb(q_pe, position_ids)
q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
compressed_kv = compressed_kv.unsqueeze(1)
k_pe, compressed_kv = past_key_value.update(k_pe, compressed_kv, self.layer_idx, cache_kwargs)
compressed_kv = compressed_kv.squeeze(1)
#if cache_position is not None:
# compressed_kv = compressed_kv[:,: cache_position[-1] + 1,:]
# k_pe = k_pe[:,:,: cache_position[-1] + 1,:]
q_absorb, out_absorb = self.get_absorbed()
q_nope = torch.matmul(q_nope, q_absorb)
attn_weights = (torch.matmul(q_pe, k_pe.mT) + torch.matmul(q_nope, compressed_kv.unsqueeze(-3).mT)) * self.softmax_scale
"""
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
raise ValueError(
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
f" {attn_weights.size()}"
)
assert attention_mask is not None
"""
if attention_mask is not None:
"""
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
"""
#causal_mask = attention_mask[:, :, :, : kv_seq_len]
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(
attn_weights, dim=-1, dtype=torch.float32
).to(q_pe.dtype)
attn_weights = nn.functional.dropout(
attn_weights, p=self.attention_dropout, training=self.training
)
attn_output = torch.einsum('bhql,blc->bhqc', attn_weights, compressed_kv)
attn_output = torch.matmul(attn_output, out_absorb.mT)
if attn_output.size() != (bsz, self.num_heads, q_len, self.v_head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.v_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.num_heads * self.v_head_dim)
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights
def forward(
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,
cache_position: Optional[torch.LongTensor] = None,
**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()
if q_len <= self.chunck_size:
return self.forward_chunck(
hidden_states,
attention_mask,
position_ids,
past_key_value,
output_attentions,
use_cache,
cache_position,
**kwargs
)
assert output_attentions == False, "output_attentions is not supported when using chunked attention"
attn_output = None
attn_weight = None
cur_idx = 0
while cur_idx < q_len:
if attention_mask is not None:
chunk_mask = attention_mask[:, :, cur_idx:min(cur_idx + self.chunck_size, q_len), ...]
else:
# generate chunk_mask automatically.
self.attn_mask = \
torch.zeros(1, 1, self.chunck_size, past_key_value.max_cache_len, device=hidden_states.device) \
if self.attn_mask is None \
else self.attn_mask
self.attn_mask[:, :, :, cur_idx:min(cur_idx+self.chunck_size, past_key_value.max_cache_len)] = \
-1e+38 * torch.triu(torch.ones(self.chunck_size, self.chunck_size, device=hidden_states.device), diagonal=1)\
[:,:min(self.chunck_size, min(past_key_value.max_cache_len-cur_idx, self.chunck_size))]
self.attn_mask[:, :, :, cur_idx+self.chunck_size:] = -1e+38
self.attn_mask[:, :, :, :cur_idx] = 0
chunk_mask = torch.narrow(self.attn_mask, 2, 0, min(self.chunck_size, q_len-cur_idx))
cur_output, cur_attn_weight = self.forward_chunck(
hidden_states[:, cur_idx:min(cur_idx + self.chunck_size, q_len), ...],
chunk_mask,
position_ids[:, cur_idx:min(cur_idx + self.chunck_size, q_len)],
past_key_value,
output_attentions,
use_cache,
cache_position[cur_idx:min(cur_idx + self.chunck_size, q_len)],
**kwargs
)
cur_idx += self.chunck_size
if attn_output is None:
attn_output = cur_output
attn_weight = cur_attn_weight
else:
attn_output = torch.cat((attn_output, cur_output), dim=-2)
attn_weight = torch.cat((attn_weight, cur_attn_weight), dim=-2)
return attn_output, attn_weight
class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention): class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
"""Multi-headed attention from 'Attention Is All You Need' paper""" """Multi-headed attention from 'Attention Is All You Need' paper"""
attn_mask: Optional[torch.Tensor] = None attn_mask: Optional[torch.Tensor] = None
......
ktransformers/operators/experts.py
View file @ 476b1d8d
...@@ -519,6 +519,7 @@ class KTransformersExperts(BaseInjectedModule, KExpertsBase): ...@@ -519,6 +519,7 @@ class KTransformersExperts(BaseInjectedModule, KExpertsBase):
from ktransformers.models.modeling_deepseek import DeepseekV2MoE from ktransformers.models.modeling_deepseek import DeepseekV2MoE
from ktransformers.models.modeling_deepseekv3 import DeepseekV3MoE
from ktransformers.models.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock from ktransformers.models.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock
from ktransformers.models.modeling_mixtral import MixtralSparseMoeBlock from ktransformers.models.modeling_mixtral import MixtralSparseMoeBlock
...@@ -727,6 +728,106 @@ class KDeepseekV2MoE(BaseInjectedModule, DeepseekV2MoE): ...@@ -727,6 +728,106 @@ class KDeepseekV2MoE(BaseInjectedModule, DeepseekV2MoE):
) )
return final_out return final_out
class KDeepseekV3MoE(BaseInjectedModule, DeepseekV3MoE):
def forward(self, hidden_states):
identity = hidden_states
orig_shape = hidden_states.shape
sequence_length = orig_shape[1]
topk_idx, topk_weight= self.gate(hidden_states)
hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
if sequence_length == 1 and hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_current_stream_capturing():
self.experts.generate_experts.submit_for_one_decode(hidden_states[0], topk_idx[0], topk_weight[0])
if self.config.n_shared_experts is not None:
y_ = self.shared_experts(identity).squeeze(0)
y = self.experts.generate_experts.sync_for_one_decode().unsqueeze(0)
y += y_
y.resize_(*orig_shape)
return y
if self.config.n_shared_experts is not None:
y_ = self.shared_experts(identity).squeeze(0)
if isinstance(self.experts, KExpertsBase):
y = self.moe_on_cpuinfer(hidden_states, topk_idx, topk_weight).view(*orig_shape).to(device=hidden_states.device)
elif hidden_states.size(0) > 10:
# TODO may bugs here
y = (
self.moe_infer(hidden_states, topk_idx, topk_weight)
.view(*orig_shape)
.to(device=hidden_states.device)
)
else:
# TODO may bugs here
y = (
self.moe_infer_simple(hidden_states, topk_idx, topk_weight)
.view(*orig_shape)
.to(device=hidden_states.device)
)
if self.config.n_shared_experts is not None:
y += y_
return y
@torch.no_grad()
def moe_on_cpuinfer(self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor) -> torch.Tensor:
outs = torch.empty_like(x)
outs = self.experts(x, topk_ids, topk_weight)
return outs
@torch.no_grad()
# TODO may bugs here
def moe_infer_simple(
self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor
) -> torch.Tensor:
"""
x: [num_tokens, hidden_size]
topk_ids, topk_weight: [num_tokens, num_selected_experts]
"""
outs = torch.zeros_like(x)
for token_idx in range(topk_ids.size(0)):
for expert_idx in range(topk_ids.size(1)):
expert = self.experts[topk_ids[token_idx, expert_idx]]
outs[token_idx] += (
expert.forward(x[token_idx]) * topk_weight[token_idx, expert_idx]
)
return outs
@torch.no_grad()
# TODO may bugs here
def moe_infer(self, x, topk_ids, topk_weight):
cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
cnts.scatter_(1, topk_ids, 1)
tokens_per_expert = cnts.sum(dim=0)
idxs = topk_ids.view(-1).argsort()
sorted_tokens = x[idxs // topk_ids.shape[1]]
tokens_per_expert = tokens_per_expert.cpu().numpy()
outputs = []
start_idx = 0
for i, num_tokens in enumerate(tokens_per_expert):
end_idx = start_idx + num_tokens
if num_tokens == 0:
continue
expert = self.experts[i + self.ep_rank * self.experts_per_rank]
tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
expert_out = expert.forward(tokens_for_this_expert)
outputs.append(expert_out)
start_idx = end_idx
outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
new_x = torch.empty_like(outs)
new_x[idxs] = outs
final_out = (
new_x.view(*topk_ids.shape, -1)
.type(topk_weight.dtype)
.mul_(topk_weight.unsqueeze(dim=-1))
.sum(dim=1)
.type(new_x.dtype)
)
return final_out
class KMistralSparseMoEBlock(BaseInjectedModule, MixtralSparseMoeBlock): class KMistralSparseMoEBlock(BaseInjectedModule, MixtralSparseMoeBlock):
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
......
ktransformers/operators/gate.py 0 → 100644
View file @ 476b1d8d
from typing import Any, Union
import numpy as np
import numpy.typing as npt
from torch import Tensor, nn
import torch.nn.functional as F
import torch
import sys, os
from ktransformers.operators.base_operator import BaseInjectedModule
sys.path.append(os.path.join(os.path.dirname(__file__), "..", "ktransformers_ext", "build"))
sys.path.append(os.path.join(os.path.dirname(__file__), "..", "ktransformers_ext", "build", "Release"))
sys.path.append(os.path.join(os.path.dirname(__file__), "..", "ktransformers_ext", "build", "Debug"))
import cpuinfer_ext
from cpuinfer_ext.moe import MOEConfig, MOE
import ctypes
from ktransformers.operators.base_operator import BaseInjectedModule
from ktransformers.util.custom_gguf import GGUFLoader
from ktransformers.models.modeling_deepseekv3 import MoEGate
from ktransformers.util.utils import InferenceState
from ktransformers.server.config.config import Config
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from abc import ABC, abstractmethod
import time
# class Base(BaseInjectedModule, ABC):
class KMoEGateBase(ABC):
def __init__(self,
key: str,
gguf_loader: GGUFLoader,
config: PretrainedConfig,
orig_module: nn.Module,
device: str = "cuda",
**kwargs):
# super().__init__(key, gguf_loader, config, orig_module, device, **kwargs)
super().__init__()
self.key = key
self.gguf_loader = gguf_loader
self.config = config
self.device = device
self.orig_module = orig_module
@abstractmethod
def forward(self, input_tensor, expert_ids, weights):
pass
@abstractmethod
def load(self, w: dict | nn.Parameter | tuple | None = None, device: str = "cpu", warmup: bool = False):
pass
@abstractmethod
def unload():
pass
def load_weights(self, override_key: str | None = None, device: str = "cpu"):
res = {}
if override_key is not None:
keys = override_key
else:
keys = [self.key]
gate = None
up = None
down = None
gate_type = None
up_type = None
down_type = None
for key in keys:
key = ".".join(key.split(".")[:-1])
if key + ".ffn_gate_inp.weight" in self.gguf_loader.tensor_info:
targets = [".ffn_gate_inp.weight", ".exp_probs_b.bias"]
tensors = self.load_multi(key, targets, device=device)
weight = tensors[".ffn_gate_inp.weight"]
e_score_correction_bias = tensors[".exp_probs_b.bias"]
weight_type = self.gguf_loader.tensor_info[key + ".ffn_gate_inp.weight"]["ggml_type"]
e_score_correction_bias_type = self.gguf_loader.tensor_info[key + ".exp_probs_b.bias"]["ggml_type"]
else:
raise ValueError(f"Experts {key} not found in gguf_loader")
res = {"weight": weight, "e_score_correction_bias": e_score_correction_bias, "weight_type": weight_type, "e_score_correction_bias_type": e_score_correction_bias_type}
return res
def load_multi(self, key: str, keys: list[str], device: str = "cpu"):
tensors = {}
for k in keys:
tensors[k] = self.gguf_loader.load_gguf_tensor(key + k, device=device)
return tensors
class KMoEGate(BaseInjectedModule, KMoEGateBase):
def __init__(
self,
key: str,
gguf_loader: GGUFLoader,
config: PretrainedConfig,
orig_module: nn.Module = None,
generate_device: str = "cuda",
prefill_device: str = "cuda",
**kwargs,
):
BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, generate_device, **kwargs)
KMoEGateBase.__init__(self, key, gguf_loader, config, orig_module, generate_device, **kwargs)
def forward(self, hidden_states) -> torch.Tensor:
return self.orig_module.forward(hidden_states)
def load(self, w: dict | nn.Parameter | tuple | None = None, device: str|None = None):
if device is None: device = self.device
if w is None: w = self.load_weights(device=device)
if isinstance(w, dict):
self.weight_type = w["weight_type"]
self.e_score_correction_bias_type = w["e_score_correction_bias_type"]
self.orig_module.weight = nn.Parameter(w["weight"])
self.orig_module.e_score_correction_bias = nn.Parameter(w["e_score_correction_bias"])
else:
raise ValueError("Invalid weight type")
self.orig_module.weight = self.orig_module.weight.to(device)
if self.topk_method == "noaux_tc":
self.orig_module.e_score_correction_bias = self.orig_module.e_score_correction_bias.to(device)
def unload(self):
if self.weight is not None:
self.weight = None
if self.topk_method == "noaux_tc":
self.e_score_correction_bias = None
ktransformers/operators/linear.py
View file @ 476b1d8d
...@@ -54,15 +54,15 @@ class KLinearBase(ABC): ...@@ -54,15 +54,15 @@ class KLinearBase(ABC):
self.has_bias = False self.has_bias = False
self.dtype = torch.get_default_dtype() self.dtype = torch.get_default_dtype()
if orig_module is not None: # if orig_module is not None:
self.in_features = orig_module.in_features # self.in_features = orig_module.in_features
self.out_features = orig_module.out_features # self.out_features = orig_module.out_features
else: # else:
shape = self.gguf_loader.tensor_info[key + ".weight"]["shape"] shape = self.gguf_loader.tensor_info[key + ".weight"]["shape"]
if len(shape) == 1: if len(shape) == 1:
print("Warning: orig_module is not set, but has in_features or out_features equals to 1, can't get in_features and out_features from GGUF") print("Warning: orig_module is not set, but has in_features or out_features equals to 1, can't get in_features and out_features from GGUF")
self.in_features = self.gguf_loader.tensor_info[key + ".weight"]["shape"][0] self.in_features = self.gguf_loader.tensor_info[key + ".weight"]["shape"][0]
self.out_features = self.gguf_loader.tensor_info[key + ".weight"]["shape"][1] self.out_features = self.gguf_loader.tensor_info[key + ".weight"]["shape"][1]
@abstractmethod @abstractmethod
def forward(self, x: torch.Tensor) -> torch.Tensor: def forward(self, x: torch.Tensor) -> torch.Tensor:
......
ktransformers/operators/models.py
View file @ 476b1d8d
...@@ -641,6 +641,7 @@ class KDeepseekV2Model(BaseInjectedModule): ...@@ -641,6 +641,7 @@ class KDeepseekV2Model(BaseInjectedModule):
if inputs_embeds is None: if inputs_embeds is None:
org_device = input_ids.device org_device = input_ids.device
# TODO move to embed_tokens's device, not hard code to cpu
input_ids = input_ids.to("cpu") input_ids = input_ids.to("cpu")
inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = self.embed_tokens(input_ids)
input_ids = input_ids.to(org_device) input_ids = input_ids.to(org_device)
...@@ -737,8 +738,9 @@ class KDeepseekV2Model(BaseInjectedModule): ...@@ -737,8 +738,9 @@ class KDeepseekV2Model(BaseInjectedModule):
hidden_states = layer_outputs[0] hidden_states = layer_outputs[0]
if use_cache: # @@@@@@@ TODO open this notes, tmp close to fit deepseekv3
next_decoder_cache = layer_outputs[2 if output_attentions else 1] # if use_cache:
# next_decoder_cache = layer_outputs[2 if output_attentions else 1]
if output_attentions: if output_attentions:
all_self_attns += (layer_outputs[1],) all_self_attns += (layer_outputs[1],)
......
ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu.yaml 0 → 100644
View file @ 476b1d8d
- match:
name: "^model.embed_tokens"
replace:
class: "default"
kwargs:
generate_device: "cpu"
prefill_device: "cpu"
- match:
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\."
class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
replace:
class: ktransformers.operators.RoPE.YarnRotaryEmbedding
kwargs:
generate_device: "cuda:0"
prefill_device: "cuda:0"
- match:
name: "^model\\.layers\\.([3456][0-9])\\."
class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
replace:
class: ktransformers.operators.RoPE.YarnRotaryEmbedding
kwargs:
generate_device: "cuda:1"
prefill_device: "cuda:1"
- match:
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.(?!self_attn\\.kv_b_proj).*$" # regular expression
class: torch.nn.Linear # only match modules matching name and class simultaneously
replace:
class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
kwargs:
generate_device: "cuda:0"
prefill_device: "cuda:0"
generate_op: "KLinearMarlin"
prefill_op: "KLinearTorch"
- match:
name: "^model\\.layers\\.([3456][0-9])\\.(?!self_attn\\.kv_b_proj).*$" # regular expression
class: torch.nn.Linear # only match modules matching name and class simultaneously
replace:
class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
kwargs:
generate_device: "cuda:1"
prefill_device: "cuda:1"
generate_op: "KLinearMarlin"
prefill_op: "KLinearTorch"
- match:
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp$"
class: ktransformers.models.modeling_deepseekv3.DeepseekV3MoE
replace:
class: ktransformers.operators.experts.KDeepseekV3MoE # mlp module with custom forward function
kwargs:
generate_device: "cuda:0"
prefill_device: "cuda:0"
- match:
name: "^model\\.layers\\.([3456][0-9])\\.mlp$"
class: ktransformers.models.modeling_deepseekv3.DeepseekV3MoE
replace:
class: ktransformers.operators.experts.KDeepseekV3MoE # mlp module with custom forward function
kwargs:
generate_device: "cuda:1"
prefill_device: "cuda:1"
- match:
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp\\.gate$"
class: ktransformers.models.modeling_deepseekv3.MoEGate
replace:
class: ktransformers.operators.gate.KMoEGate
kwargs:
generate_device: "cuda:0"
prefill_device: "cuda:0"
- match:
name: "^model\\.layers\\.([3456][0-9])\\.mlp\\.gate$"
class: ktransformers.models.modeling_deepseekv3.MoEGate
replace:
class: ktransformers.operators.gate.KMoEGate # mlp module with custom forward function
kwargs:
generate_device: "cuda:1"
prefill_device: "cuda:1"
- match:
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp\\.experts$"
replace:
class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
kwargs:
prefill_device: "cuda:0"
prefill_op: "KExpertsTorch"
generate_device: "cpu"
generate_op: "KExpertsCPU"
out_device: "cuda:0"
recursive: False # don't recursively inject submodules of this module
- match:
name: "^model\\.layers\\.([3456][0-9])\\.mlp\\.experts$"
replace:
class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
kwargs:
prefill_device: "cuda:1"
prefill_op: "KExpertsTorch"
generate_device: "cpu"
generate_op: "KExpertsCPU"
out_device: "cuda:1"
recursive: False # don't recursively inject submodules of this module
- match:
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.self_attn$"
replace:
class: ktransformers.operators.attention.KDeepseekV3Attention # optimized MLA implementation
kwargs:
generate_device: "cuda:0"
prefill_device: "cuda:0"
- match:
name: "^model\\.layers\\.([3456][0-9])\\.self_attn$"
replace:
class: ktransformers.operators.attention.KDeepseekV3Attention # optimized MLA implementation
kwargs:
generate_device: "cuda:1"
prefill_device: "cuda:1"
- match:
name: "^model$"
replace:
class: "ktransformers.operators.models.KDeepseekV2Model"
kwargs:
per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
transfer_map:
30: "cuda:1"
- match:
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\."
replace:
class: "default"
kwargs:
generate_device: "cuda:0"
prefill_device: "cuda:0"
- match:
name: "(^model\\.layers\\.([3456][0-9])\\.)|(model.norm)|(lm_head)"
replace:
class: "default"
kwargs:
generate_device: "cuda:1"
prefill_device: "cuda:1"
ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml 0 → 100644
View file @ 476b1d8d
- match:
class: ktransformers.models.modeling_deepseek.DeepseekV3YarnRotaryEmbedding
replace:
class: ktransformers.operators.RoPE.YarnRotaryEmbedding
kwargs:
generate_device: "cuda"
prefill_device: "cuda"
- match:
name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$" # regular expression
class: torch.nn.Linear # only match modules matching name and class simultaneously
replace:
class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
kwargs:
generate_device: "cuda"
prefill_device: "cuda"
generate_op: "KLinearMarlin"
prefill_op: "KLinearTorch"
- match:
name: "^model\\.layers\\..*\\.mlp$"
class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
replace:
class: ktransformers.operators.experts.KDeepseekV2MoE # mlp module with custom forward function
kwargs:
generate_device: "cuda"
prefill_device: "cuda"
- match:
name: "^model\\.layers\\..*\\.mlp\\.experts$"
replace:
class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
kwargs:
prefill_device: "cuda"
prefill_op: "KExpertsTorch"
generate_device: "cpu"
generate_op: "KExpertsCPU"
out_device: "cuda"
recursive: False # don't recursively inject submodules of this module
- match:
name: "^model\\.layers\\..*\\.self_attn$"
replace:
class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
kwargs:
generate_device: "cuda"
prefill_device: "cuda"
- match:
name: "^model$"
replace:
class: "ktransformers.operators.models.KDeepseekV2Model"
kwargs:
per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
- match:
name: "^model.embed_tokens"
replace:
class: "default"
kwargs:
generate_device: "cpu"
prefill_device: "cpu"
\ No newline at end of file
ktransformers/server/backend/interfaces/ktransformers.py
View file @ 476b1d8d
...@@ -46,17 +46,26 @@ class KTransformersInterface(TransformersInterface): ...@@ -46,17 +46,26 @@ class KTransformersInterface(TransformersInterface):
) )
optimize_and_load_gguf(self.model, optimize_rule_path, gguf_path, config) optimize_and_load_gguf(self.model, optimize_rule_path, gguf_path, config)
device_map = self.model.gguf_loader.tensor_device_map self.device_map = self.model.gguf_loader.tensor_device_map
logger.info(f"{args.model_name} loaded from {args.model_dir} to {device_map}") # logger.info(f"{args.model_name} loaded from {args.model_dir} to {self.device_map}")
self.cache = StaticCache( self.cache = StaticCache(
config=self.model.config, config=self.model.config,
max_batch_size=args.batch_size, max_batch_size=args.batch_size,
max_cache_len=args.cache_lens, max_cache_len=args.cache_lens,
device=device_map, device=self.device_map,
dtype=self.model.dtype, dtype=self.model.dtype,
) )
logger.info(f"StaticCache (length={args.cache_lens}) created at {device_map}, batch size:{args.batch_size}") # logger.info(f"StaticCache (length={args.cache_lens}), batch size:{args.batch_size}")
self.model.generation_config = GenerationConfig.from_pretrained(args.model_dir) try:
self.model.generation_config = GenerationConfig.from_pretrained(args.model_dir)
except:
gen_config = GenerationConfig(
max_length=128,
temperature=0.7,
top_p=0.9,
do_sample=True
)
self.model.generation_config = gen_config
if self.model.generation_config.pad_token_id is None: if self.model.generation_config.pad_token_id is None:
self.model.generation_config.pad_token_id = self.model.generation_config.eos_token_id self.model.generation_config.pad_token_id = self.model.generation_config.eos_token_id
self.streamer = TextStreamer(self.tokenizer) self.streamer = TextStreamer(self.tokenizer)
...@@ -102,3 +111,63 @@ class KTransformersInterface(TransformersInterface): ...@@ -102,3 +111,63 @@ class KTransformersInterface(TransformersInterface):
logits = logits[0, -1, :] logits = logits[0, -1, :]
return self.logits_to_token(logits) return self.logits_to_token(logits)
@torch.no_grad
def prefill(self, input_ids: torch.Tensor, is_new: bool):
input_ids_length = input_ids.shape[-1]
self.profiler.set_counter("prefill", input_ids_length)
logger.debug(f"input_ids: {input_ids.shape}")
device = self.device_map.get("blk.0.self_attn", {}).get("generate_device", "cuda:0")
if is_new:
self.cache.reset()
self.ever_generated_ids.clear()
former_seq_length = 0
self.seq_length = input_ids_length
self.generated_ids = torch.zeros(
self.args.batch_size,
self.seq_length + self.args.max_new_tokens + 1,
dtype=torch.int,
device=self.args.device,
)
else:
logger.debug(f"generate_ids: {self.generated_ids.shape}")
former_seq_length = self.seq_length
self.seq_length += input_ids_length
expected_length = self.seq_length + self.args.max_new_tokens + 1
delta_length = expected_length - self.generated_ids.shape[-1]
if delta_length > 0:
new_generate_ids = torch.zeros(
self.args.batch_size, delta_length, dtype=torch.int, device=self.args.device
)
self.generated_ids = torch.cat([self.generated_ids, new_generate_ids], dim=-1)
logger.debug(f"cache position: {former_seq_length} to {self.seq_length}")
cache_position = torch.arange(former_seq_length, self.seq_length, device=device)
self.generated_ids[:, cache_position] = input_ids.to(self.args.device).to(torch.int)
mask = torch.ones((1, self.seq_length)).to(device)
if not (type(self) is TransformersInterface):
input_ids = input_ids.to("cpu")
inputs_embeds = self.model.model.embed_tokens(input_ids).to(device)
if self.use_static_cache:
logits = self.model(
inputs_embeds=inputs_embeds,
cache_position=cache_position,
past_key_values=self.cache,
return_dict=False,
use_cache=True,
attention_mask=mask,
)[0]
else:
logits = self.model(inputs_embeds=inputs_embeds, return_dict=False)[0]
next_token = self.logits_to_token(logits[0, -1, :])
yield self.append_new_tokens(next_token)
@property
def active_cache_position(self):
device = self.device_map.get("blk.0.self_attn", {}).get("generate_device", "cuda:0")
return torch.tensor([self.seq_length - 1], device=device)
\ No newline at end of file
ktransformers/server/backend/interfaces/transformers.py
View file @ 476b1d8d
...@@ -134,7 +134,7 @@ class TransformersInterface(BackendInterfaceBase): ...@@ -134,7 +134,7 @@ class TransformersInterface(BackendInterfaceBase):
self.tokenizer = AutoTokenizer.from_pretrained(args.model_dir) self.tokenizer = AutoTokenizer.from_pretrained(args.model_dir)
self.model = AutoModelForCausalLM.from_pretrained(args.model_dir, device_map=args.device, use_safetensors=True) self.model = AutoModelForCausalLM.from_pretrained(args.model_dir, device_map=args.device, use_safetensors=True)
logger.info(f"{args.model_name} loaded from {args.model_dir} to {args.device}") # logger.info(f"{args.model_name} loaded from {args.model_dir} to {args.device}")
self.cache = StaticCache( self.cache = StaticCache(
config=self.model.config, config=self.model.config,
...@@ -143,7 +143,7 @@ class TransformersInterface(BackendInterfaceBase): ...@@ -143,7 +143,7 @@ class TransformersInterface(BackendInterfaceBase):
device=args.device, device=args.device,
dtype=self.model.dtype, dtype=self.model.dtype,
) )
logger.info(f"StaticCache (length={args.cache_lens}) created at {args.device}, batch size:{args.batch_size}") # logger.info(f"StaticCache (length={args.cache_lens}) created at {args.device}, batch size:{args.batch_size}")
self.streamer = TextStreamer(self.tokenizer) self.streamer = TextStreamer(self.tokenizer)
......
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