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refactor to simplify following upgrade 2 (#12685)

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Yishuo Wang 2025-01-10 09:29:03 +08:00 committed by GitHub
parent 2673792de6
commit 68857494a5
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6 changed files with 33 additions and 376 deletions
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13
python/llm/src/ipex_llm/transformers/convert.py
View file

@ -1590,6 +1590,9 @@ def _optimize_post(model):
convert_forward(model,
module.Qwen2ForCausalLM,
qwen2_causal_lm_forward)
convert_forward(model,
module.Qwen2Model,
qwen2_model_forward)
convert_forward(model,
module.Qwen2RMSNorm,
rms_norm_forward)
@ -1602,12 +1605,6 @@ def _optimize_post(model):
convert_forward(model,
module.Qwen2SdpaAttention,
qwen2_attention_forward)
if version.parse(trans_version) >= version.parse("4.42"):
from ipex_llm.transformers.models.qwen2 import qwen2_model_forward_4_42
convert_forward(model, module.Qwen2Model, qwen2_model_forward_4_42)
else:
from ipex_llm.transformers.models.qwen2 import qwen2_model_forward
convert_forward(model, module.Qwen2Model, qwen2_model_forward)
elif model.config.model_type == "qwen2_moe":
# for Qwen1.5-MOE-A2.7B
modeling_module_name = model.__class__.__module__
@ -1819,9 +1816,7 @@ def _optimize_post(model):
from ipex_llm.transformers.models.phi3 import attention_forward
convert_forward(model, module.Phi3Attention, attention_forward)
convert_forward(model, module.Phi3SdpaAttention, attention_forward)
from ipex_llm.transformers.models.phi3 import mlp_forward
convert_forward(model, module.Phi3MLP, mlp_forward)
from ipex_llm.transformers.models.common import rms_norm_forward
convert_forward(model, module.Phi3MLP, mlp_silu_forward)
convert_forward(model, module.Phi3RMSNorm, rms_norm_forward)
if model.config.model_type == "phi3":
from ipex_llm.transformers.models.phi3 import phi3_model_forward_wrapper

3
python/llm/src/ipex_llm/transformers/models/baichuan.py
View file

@ -30,8 +30,7 @@ from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp
from ipex_llm.transformers.models.utils import update_past_key_value
from ipex_llm.transformers.models.utils import should_use_fuse_rope
from ipex_llm.transformers.models.utils import use_sdp
from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, SILU
from ipex_llm.transformers.models.utils import mlp_fusion_check
from ipex_llm.transformers.models.utils import apply_rotary_pos_emb
from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_36
from ipex_llm.transformers.kv import DynamicCompressFp8Cache, DynamicCompressCache
import warnings

15
python/llm/src/ipex_llm/transformers/models/internlm.py
View file

@ -113,21 +113,6 @@ def internlm_attention_forward(
return attn_output, attn_weights, past_key_value
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to
(batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads,
n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
def internlm2_attention_forward(
self,
hidden_states: torch.Tensor,

21
python/llm/src/ipex_llm/transformers/models/phi3.py
View file

@ -39,7 +39,6 @@ import warnings
from ipex_llm.transformers.models.common import attention_softmax
from ipex_llm.transformers.models.common import scaled_dot_product_attention
from ipex_llm.transformers.models.utils import should_use_fuse_rope, rotate_half
from ipex_llm.transformers.models.utils import mlp_fusion_check, SILU
from ipex_llm.transformers.models.utils import use_sdp, use_sdp_causal
from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
from ipex_llm.transformers.models.utils import should_use_compresskv, is_enough_kv_cache_room_4_36
@ -213,24 +212,8 @@ def split_mlp(module: torch.nn.Module):
del module.gate_up_proj
def mlp_forward(
self,
hidden_states: torch.FloatTensor
) -> torch.FloatTensor:
x_2d = hidden_states.view(-1, hidden_states.shape[-1])
qtype = getattr(self.gate_proj, "qtype", None)
if mlp_fusion_check(x_2d, qtype, self.training):
x_2d = x_2d.contiguous()
import xe_linear
return self.down_proj(xe_linear.mlp_forward_xpu(
x_2d, self.gate_proj.weight.data, self.up_proj.weight.data,
x_2d.shape[0], x_2d.shape[1], self.gate_proj.out_features,
SILU, qtype
))
return self.down_proj(
self.activation_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states)
)
# rename activation function
module.act_fn = module.activation_fn
def phi3_model_forward_wrapper(origin_model_forward):

337
python/llm/src/ipex_llm/transformers/models/qwen2.py
View file

@ -51,16 +51,11 @@ from ipex_llm.transformers.models.utils import use_quantize_kv_cache, \
should_use_compresskv, is_enough_kv_cache_room_4_36
from ipex_llm.transformers.kv import DynamicFp8Cache, DynamicNormalCache, \
DynamicCompressCache, DynamicCompressFp8Cache
from ipex_llm.utils.common import invalidInputError
from transformers.models.qwen2.modeling_qwen2 import Qwen2Attention, Qwen2MLP
from transformers.models.qwen2.modeling_qwen2 import Qwen2Model, Qwen2Attention, Qwen2MLP
from transformers.models.qwen2.modeling_qwen2 import apply_rotary_pos_emb
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.cache_utils import Cache
from transformers import logging
logger = logging.get_logger(__name__)
def qwen2_model_forward(
@ -74,50 +69,18 @@ def qwen2_model_forward(
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None, # for transformers >= 4.42
cache_position: Optional[torch.LongTensor] = 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 decoder_input_ids or decoder_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
past_key_values_length = 0
# ipex-llm changes start
# IPEX-LLM OPT: kv cache and quantize kv cache
# IPEX-LLM OPT start: kv cache and quantize kv cache
inputs = input_ids if input_ids is not None else inputs_embeds
num_heads, num_kv_heads = self.config.num_attention_heads, self.config.num_key_value_heads
use_quantize_kv = (
self.config.hidden_size != 3584 # disable quantize kv in specific model
and use_quantize_kv_cache(self.layers[0].mlp.up_proj, inputs, num_heads, num_kv_heads)
use_cache = use_cache if use_cache is not None else self.config.use_cache
use_cache = True if inputs.device.type == "xpu" else use_cache
use_quantize_kv = self.config.hidden_size != 3584 and use_quantize_kv_cache(
self.layers[0].mlp.down_proj, inputs,
self.config.num_attention_heads, self.config.num_key_value_heads
)
use_compress_kv = should_use_compresskv(inputs, inputs.shape[1]) or \
isinstance(past_key_values, DynamicCompressCache)
@ -133,274 +96,26 @@ def qwen2_model_forward(
if not use_quantize_kv and not use_compress_kv and not isinstance(past_key_values,
DynamicNormalCache):
past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
past_key_values_length = past_key_values.get_usable_length(seq_length)
# ipex-llm changes end
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)
# `cache_position` is required after transformers 4.42
if cache_position is not None:
kwargs = {"cache_position": cache_position}
else:
position_ids = position_ids.view(-1, seq_length).long()
kwargs = {}
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
flash_attn_2 = self._attn_implementation == "flash_attention_2"
if attention_mask is not None and flash_attn_2 and use_cache:
is_padding_right = attention_mask[:, -1].sum().item() != batch_size
if is_padding_right:
invalidInputError(
False,
"You are attempting to perform batched generation with padding_side='right'"
" this may lead to unexpected behaviour for Flash Attention version of Qwen2."
" Make sure to call `tokenizer.padding_side = 'left'` before tokenizing "
"the input. "
)
from transformers.models.qwen2.modeling_qwen2 import _prepare_4d_causal_attention_mask_for_sdpa
from transformers.models.qwen2.modeling_qwen2 import _prepare_4d_causal_attention_mask
# ipex-llm changes start: don't generate `attention_mask` in decode phase
if seq_length == 1:
attention_mask = None
# ipex-llm changes end
elif self._attn_implementation == "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._attn_implementation == "sdpa" and not output_attentions:
# output_attentions=True can not be supported when using SDPA, and we fall back on
# the manual implementation that requires a 4D causal mask in all cases.
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
attention_mask,
(batch_size, seq_length),
inputs_embeds,
past_key_values_length,
)
else:
# 4d mask is passed through the layers
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask,
(batch_size, seq_length),
inputs_embeds,
past_key_values_length,
sliding_window=self.config.sliding_window,
)
hidden_states = inputs_embeds
# decoder layers
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:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
)
else:
# ipex-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)
# ipex-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,)
# ipex-llm changes start: remove `to_legacy_cache`
next_cache = None
if use_cache:
next_cache = next_decoder_cache
# ipex-llm changes end
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 qwen2_model_forward_4_42(
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,
cache_position: Optional[torch.LongTensor] = 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
invalidInputError(
(input_ids is None) ^ (inputs_embeds is None),
"You cannot specify both input_ids and inputs_embeds at the same time, "
"and must specify either one"
)
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
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# ipex-llm changes start
# IPEX-LLM OPT: kv cache and quantize kv cache
num_heads, num_kv_heads = self.config.num_attention_heads, self.config.num_key_value_heads
use_quantize_kv = (
self.config.hidden_size != 3584 # disable quantize kv in specific model
and use_quantize_kv_cache(self.layers[0].mlp.up_proj, inputs_embeds,
num_heads, num_kv_heads)
)
use_compress_kv = should_use_compresskv(inputs_embeds, inputs_embeds.shape[1]) or \
isinstance(past_key_values, DynamicCompressCache)
if use_cache:
if use_compress_kv and not isinstance(past_key_values, DynamicCompressCache):
if use_quantize_kv:
past_key_values = DynamicCompressFp8Cache.from_legacy_cache(past_key_values)
else:
past_key_values = DynamicCompressCache.from_legacy_cache(past_key_values)
elif use_quantize_kv and not use_compress_kv and not isinstance(past_key_values,
DynamicFp8Cache):
past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
if not use_quantize_kv and not use_compress_kv and not isinstance(past_key_values,
DynamicNormalCache):
past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
# ipex-llm changes end
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# decoder layers
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:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
cache_position,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
)
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,)
# ipex-llm changes start: remove `to_legacy_cache`
next_cache = None
if use_cache:
next_cache = next_decoder_cache
# ipex-llm changes end
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,
return Qwen2Model.forward(
self=self,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs
)

20
python/llm/src/ipex_llm/transformers/models/utils.py
View file

@ -272,26 +272,6 @@ def use_xmx(x: torch.Tensor, qtype: int):
)
def fp16_fusion_check(proj, x, training):
# only use fp16 fusion on PVC inference
if proj is None:
return False
if not hasattr(proj, "qtype"):
return False
if proj.qtype != ggml_tensor_qtype["fp16"]:
return False
if proj.weight_type != 2:
return False
if training:
return False
if x.requires_grad:
return False
device_type = get_xpu_device_name(x.device)
if device_type != "pvc":
return False
return True
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1: