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refactor yuan2 and starcoder2 and fix #12589

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Dec 20, 2024
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refactor yuan2 and starcoder2 and fix #12589

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2 changes: 1 addition & 1 deletion python/llm/src/ipex_llm/transformers/models/llama32.py
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Original file line number Diff line number Diff line change
Expand Up @@ -234,7 +234,7 @@ def llama_attention_forward(
attn_weights = None
attn_output = scaled_dot_product_attention(
query_states, key_states, value_states,
attention_mask, q_len == key_states.size(2), math.sqrt(self.head_dim)
attention_mask, q_len == key_states.size(2)
)

attn_output = attn_output.transpose(1, 2).contiguous()
Expand Down
9 changes: 3 additions & 6 deletions python/llm/src/ipex_llm/transformers/models/minicpm.py
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Expand Up @@ -38,15 +38,13 @@

import torch
import warnings
import torch.nn as nn
from typing import Optional, Tuple, Union, List
import math
from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, is_enough_kv_cache_room_4_36
from ipex_llm.transformers.models.utils import use_sdp, use_sdp_causal, use_quantize_kv_cache
from ipex_llm.transformers.models.utils import restore_fp8_kv_cache, get_compresskv_attn_mask
from ipex_llm.transformers.models.utils import use_quantize_kv_cache
from ipex_llm.transformers.models.utils import should_use_compresskv, should_use_fuse_rope
from ipex_llm.transformers.models.llama import repeat_kv
from ipex_llm.transformers.models.common import merge_qkv_base
from ipex_llm.transformers.models.common import scaled_dot_product_attention
from ipex_llm.transformers.kv import DynamicNormalCache, DynamicFp8Cache, \
DynamicCompressCache, DynamicCompressFp8Cache
from transformers.cache_utils import Cache
Expand Down Expand Up @@ -127,11 +125,10 @@ def minicpm_attention_forward(
key_states, value_states = past_key_value.update(key_states, value_states,
self.layer_idx, None)

from ipex_llm.transformers.models.common import scaled_dot_product_attention
attn_weights = None
attn_output = scaled_dot_product_attention(
query_states, key_states, value_states,
attention_mask, q_len == kv_seq_len, math.sqrt(self.head_dim)
attention_mask, q_len == kv_seq_len
)

attn_output = attn_output.transpose(1, 2).contiguous()
Expand Down
8 changes: 5 additions & 3 deletions python/llm/src/ipex_llm/transformers/models/minicpmv.py
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Expand Up @@ -28,6 +28,7 @@
from torch.nn.functional import linear
from ipex_llm.transformers.models.common import merge_qkv_base, padding_qkv_hd
from ipex_llm.transformers.models.common import attention_softmax
from ipex_llm.transformers.models.common import scaled_dot_product_attention
from transformers import AutoProcessor, TextIteratorStreamer
from transformers.generation.logits_process import RepetitionPenaltyLogitsProcessor

Expand Down Expand Up @@ -72,10 +73,11 @@ def siglip_attention_forward(
72, 80
)

from ipex_llm.transformers.models.common import scaled_dot_product_attention
attn_weights = None
attn_output = scaled_dot_product_attention(query_states, key_states, value_states,
attention_mask, False, math.sqrt(self.head_dim))
attn_output = scaled_dot_product_attention(
query_states, key_states, value_states,
attention_mask, False, 1 / math.sqrt(self.head_dim)
)

attn_output = attn_output[:, :, :, :self.head_dim]

Expand Down
2 changes: 1 addition & 1 deletion python/llm/src/ipex_llm/transformers/models/qwen2.py
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Expand Up @@ -595,7 +595,7 @@ def qwen2_attention_forward(
else:
attn_output = scaled_dot_product_attention(
query_states, key_states, value_states,
attention_mask, q_len == kv_seq_len, math.sqrt(self.head_dim)
attention_mask, q_len == kv_seq_len
)

attn_output = attn_output.transpose(1, 2).contiguous()
Expand Down
50 changes: 9 additions & 41 deletions python/llm/src/ipex_llm/transformers/models/starcoder2.py
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Expand Up @@ -40,17 +40,15 @@
import torch
import warnings

from ipex_llm.transformers.models.common import merge_qkv_base, attention_softmax
from ipex_llm.transformers.models.utils import (
use_quantize_kv_cache, restore_fp8_kv_cache,
should_use_fuse_rope, use_sdp, use_sdp_causal
)
from ipex_llm.transformers.models.common import merge_qkv_base
from ipex_llm.transformers.models.common import scaled_dot_product_attention
from ipex_llm.transformers.models.utils import use_quantize_kv_cache, should_use_fuse_rope
from ipex_llm.transformers.kv import DynamicFp8Cache, DynamicNormalCache
from ipex_llm.utils.common.log4Error import invalidInputError

from typing import Optional, Tuple, List
from transformers.cache_utils import Cache
from transformers.models.starcoder2.modeling_starcoder2 import repeat_kv, apply_rotary_pos_emb
from transformers.models.starcoder2.modeling_starcoder2 import apply_rotary_pos_emb
from transformers.models.starcoder2.modeling_starcoder2 import Starcoder2Model, Starcoder2Attention


Expand Down Expand Up @@ -103,41 +101,11 @@ def attention_forward(
self.layer_idx, None)

# IPEX-LLM OPT: sdp
if use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
import xe_addons
if isinstance(past_key_value, DynamicFp8Cache):
attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states,
attention_mask)
else:
attn_output = xe_addons.sdp(query_states, key_states, value_states,
attention_mask)
elif use_sdp_causal(q_len, kv_seq_len, self.head_dim, query_states, self.training):
import xe_addons
if isinstance(past_key_value, DynamicFp8Cache):
attn_output = xe_addons.sdp_fp8_causal(query_states, key_states,
value_states, attention_mask)
else:
attn_output = xe_addons.sdp_causal(query_states, key_states,
value_states, attention_mask)
else:
if isinstance(past_key_value, DynamicFp8Cache):
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_weights = torch.matmul(query_states,
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

if attention_mask is not None:
attn_weights = attn_weights + attention_mask

# upcast attention to fp32
attn_weights = attention_softmax(attn_weights)
attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout,
training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
attn_weights = None
attn_output = scaled_dot_product_attention(
query_states, key_states, value_states,
attention_mask, q_len == kv_seq_len
)

attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
Expand Down
40 changes: 9 additions & 31 deletions python/llm/src/ipex_llm/transformers/models/yuan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,12 +26,12 @@
import torch

from ipex_llm.utils.common import invalidInputError
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 apply_rotary_pos_emb, \
mlp_fusion_check, fp16_fusion_check
from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
from ipex_llm.transformers.models.utils import use_quantize_kv_cache
from ipex_llm.transformers.models.utils import SILU, update_past_key_value
from ipex_llm.transformers.models.utils import should_use_fuse_rope, use_sdp, use_sdp_causal
from ipex_llm.transformers.models.utils import should_use_fuse_rope


def merge_qk(module: torch.nn.Module):
Expand Down Expand Up @@ -214,34 +214,12 @@ def yuan_attention_forward(
)
past_key_value = (key_states, value_states, before_hidden_states) if use_cache else None

# IPEX-LLM OPT: sdp
if use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
import xe_addons
if use_quantize_kv:
attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states,
attention_mask)
else:
attn_output = xe_addons.sdp(query_states, key_states, value_states,
attention_mask)
elif use_sdp_causal(q_len, kv_seq_len, self.head_dim, query_states, self.training):
import xe_addons
if use_quantize_kv:
attn_output = xe_addons.sdp_fp8_causal(query_states, key_states,
value_states, attention_mask)
else:
attn_output = xe_addons.sdp_causal(query_states, key_states,
value_states, attention_mask)
else:
if use_quantize_kv:
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
query_states.dtype)
attn_weights = torch.matmul(query_states,
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = attention_softmax(attn_weights)
attn_output = torch.matmul(attn_weights, value_states)
# IPEX-LLM OPT: sdpa
attn_weights = None
attn_output = scaled_dot_product_attention(
query_states, key_states, value_states,
attention_mask, q_len == kv_seq_len
)

attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
Expand Down
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