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fix chatglm2/3-32k/128k fp16 (#11311)

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Yishuo Wang 2024-06-14 09:58:07 +08:00 committed by GitHub
parent 1b0c4c8cb8
commit 7f65836cb9
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2 changed files with 9 additions and 43 deletions
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9
python/llm/src/ipex_llm/transformers/models/chatglm2.py
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@ -98,14 +98,15 @@ def chatglm2_model_forward(
dtype=torch.int64, device=inputs_embeds.device) dtype=torch.int64, device=inputs_embeds.device)
position_ids = position_ids.repeat(batch_size, 1) position_ids = position_ids.repeat(batch_size, 1)
if getattr(self.rotary_pos_emb, "cached_dtype", None) != inputs_embeds.dtype: if not getattr(self.rotary_pos_emb, "cached", False):
rot_dim = self.rotary_pos_emb.dim rot_dim = self.rotary_pos_emb.dim
base = 10000 * getattr(self.rotary_pos_emb, "rope_ratio", 1) base = 10000 * getattr(self.rotary_pos_emb, "rope_ratio", 1)
inv_freq = 1.0 / (base ** (torch.arange(0, rot_dim, 2, inv_freq = 1.0 / (base ** (torch.arange(0, rot_dim, 2,
device=inputs_embeds.device, dtype=torch.float,
dtype=inputs_embeds.dtype) / rot_dim)) device=inputs_embeds.device) / rot_dim))
inv_freq = inv_freq.to(inputs_embeds.dtype)
self.rotary_pos_emb.register_buffer("inv_freq", inv_freq, persistent=False) self.rotary_pos_emb.register_buffer("inv_freq", inv_freq, persistent=False)
self.rotary_pos_emb.cached_dtype = inputs_embeds.dtype self.rotary_pos_emb.cached = True
# `full_attention_mask` is not None only when # `full_attention_mask` is not None only when
# `past_key_values` is not None and `seq_length` > 1 # `past_key_values` is not None and `seq_length` > 1

43
python/llm/src/ipex_llm/transformers/models/chatglm4.py
View file

@ -18,8 +18,7 @@
# #
import torch import torch
from typing import Optional, Tuple, Union, List, Callable, Dict, Any from typing import Optional, Tuple, Union
import torch.nn.functional as F
from ipex_llm.transformers.models.utils import restore_fp8_kv_cache, update_past_key_value from ipex_llm.transformers.models.utils import restore_fp8_kv_cache, update_past_key_value
from ipex_llm.transformers.models.utils import use_quantize_kv_cache, use_sdp, use_sdp_causal from ipex_llm.transformers.models.utils import use_quantize_kv_cache, use_sdp, use_sdp_causal
from ipex_llm.transformers.models.utils import should_use_fuse_rope, apply_rotary_pos_emb from ipex_llm.transformers.models.utils import should_use_fuse_rope, apply_rotary_pos_emb
@ -27,11 +26,6 @@ from ipex_llm.transformers.models.chatglm2 import repeat_kv
from transformers.modeling_outputs import BaseModelOutputWithPast from transformers.modeling_outputs import BaseModelOutputWithPast
import math import math
import os
KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
KV_CACHE_ALLOC_MIN_LENGTH = 512
def chatglm4_model_forward( def chatglm4_model_forward(
self, self,
@ -45,34 +39,6 @@ def chatglm4_model_forward(
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]: ) -> Union[Tuple, BaseModelOutputWithPast]:
from ipex_llm.transformers.kv import DynamicFp8Cache
use_cache = use_cache if use_cache is not None else self.config.use_cache
return chatglm4_model_forward_internal(
self=self,
input_ids=input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
full_attention_mask=full_attention_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
def chatglm4_model_forward_internal(
self,
input_ids,
position_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.BoolTensor] = None,
full_attention_mask: Optional[torch.BoolTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]=None,
inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else output_hidden_states if output_hidden_states is not None else
self.config.output_hidden_states self.config.output_hidden_states
@ -104,16 +70,15 @@ def chatglm4_model_forward_internal(
dtype=torch.int64, device=inputs_embeds.device) dtype=torch.int64, device=inputs_embeds.device)
position_ids = position_ids.repeat(batch_size, 1) position_ids = position_ids.repeat(batch_size, 1)
if getattr(self.rotary_pos_emb, "cached_dtype", None) != inputs_embeds.dtype: if not getattr(self.rotary_pos_emb, "cached", False):
rot_dim = self.rotary_pos_emb.dim rot_dim = self.rotary_pos_emb.dim
base = 10000 * getattr(self.rotary_pos_emb, "rope_ratio", 1) base = 10000 * getattr(self.rotary_pos_emb, "rope_ratio", 1)
# We should generate float inv_freq to avoid overflow, as base is too large. # We should generate float inv_freq to avoid overflow, as base is too large.
inv_freq = 1.0 / (base ** (torch.arange(0, rot_dim, 2, inv_freq = 1.0 / (base ** (torch.arange(0, rot_dim, 2,
dtype=torch.float, dtype=torch.float,
device=inputs_embeds.device) / rot_dim)) device=inputs_embeds.device) / rot_dim))
self.rotary_pos_emb.register_buffer("inv_freq", inv_freq = inv_freq.to(inputs_embeds.dtype)
inv_freq.to(inputs_embeds.dtype), self.rotary_pos_emb.register_buffer("inv_freq", inv_freq, persistent=False)
persistent=False)
self.rotary_pos_emb.cached = True self.rotary_pos_emb.cached = True
# `full_attention_mask` is not None only when # `full_attention_mask` is not None only when