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refactor chatglm2, internlm, stablelm and qwen #12604

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refactor chatglm2, internlm, stablelm and qwen #12604

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a828648
refactor chatglm2, internlm, stablelm and qwen
MeouSker77 Dec 24, 2024
2baf336
fix
MeouSker77 Dec 24, 2024
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86 changes: 13 additions & 73 deletions python/llm/src/ipex_llm/transformers/models/chatglm2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,17 +18,16 @@
#

import os
import math
import torch
from typing import Optional, Tuple
from transformers.modeling_outputs import BaseModelOutputWithPast
from ipex_llm.utils.common.log4Error import invalidInputError
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.common import scaled_dot_product_attention
from ipex_llm.transformers.models.utils import update_past_key_value
from ipex_llm.transformers.models.utils import use_quantize_kv_cache
from ipex_llm.transformers.models.utils import should_use_fuse_rope, apply_rotary_pos_emb
from ipex_llm.transformers.models.utils import mlp_fusion_check, SILU
from ipex_llm.transformers.models.utils import use_quantize_kv_cache, use_sdp, \
use_sdp_causal, should_use_compresskv, is_enough_kv_cache_room_4_36
from ipex_llm.transformers.models.utils import use_quantize_kv_cache
from ipex_llm.transformers.models.utils import should_use_compresskv, is_enough_kv_cache_room_4_36
from ipex_llm.transformers.kv import DynamicCompressCache, DynamicCompressFp8Cache

KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
Expand Down Expand Up @@ -310,50 +309,10 @@ def chatglm2_attention_forward(
value_states.permute(2, 0, 1, 3)) if use_cache else None

# IPEX-LLM OPT: sdp
attn_weights = None
if use_sdp(q_len, kv_seq_len, head_dim, query_states):
import xe_addons
if use_compresskv and attention_mask is not None:
attention_mask = None
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, 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)
elif query_states.device.type == "cpu":
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, n_head // n_kv_head)
value_states = repeat_kv(value_states, n_head // n_kv_head)
if q_len == kv_seq_len:
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states, key_states, value_states, is_causal=True
)
else:
attn_output = torch.nn.functional.scaled_dot_product_attention(
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)
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, n_head // n_kv_head)
value_states = repeat_kv(value_states, n_head // n_kv_head)

attn_weights = torch.matmul(query_states,
key_states.transpose(2, 3)) / math.sqrt(head_dim)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
dtype=torch.float32).to(value_states.dtype)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = scaled_dot_product_attention(
query_states, key_states, value_states,
attention_mask, q_len == kv_seq_len
)

# context_layer's shape: [bsz, n_head, seq_len, head_dim] -> [seq_len, bsz, n_head * head_dim]
attn_output = attn_output.permute(2, 0, 1, 3).contiguous().view(q_len, bsz, n_head * head_dim)
Expand Down Expand Up @@ -541,29 +500,10 @@ def codegeex_attention_forward(
# =================
# Output. [sq, b, h]
# =================
context_layer = None
if use_sdp(q_len, kv_seq_len, head_dim, query_layer):
import xe_addons
context_layer = xe_addons.sdp(query_layer, key_layer, value_layer, attention_mask)
elif use_sdp_causal(q_len, kv_seq_len, head_dim, query_layer, self.training):
import xe_addons
context_layer = xe_addons.sdp_causal(query_layer, key_layer, value_layer, attention_mask)
else:
# repeat k/v heads if n_kv_heads < n_heads
key_layer = repeat_kv(key_layer, n_head // n_kv_head)
value_layer = repeat_kv(value_layer, n_head // n_kv_head)
if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer,
key_layer,
value_layer,
is_causal=True)
else:
if attention_mask is not None:
attention_mask = ~attention_mask
context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer,
key_layer,
value_layer,
attention_mask)
context_layer = scaled_dot_product_attention(
query_layer, key_layer, value_layer,
attention_mask, q_len == kv_seq_len
)

context_layer = context_layer.permute(2, 0, 1, 3).contiguous().view(q_len,
bsz,
Expand Down
114 changes: 16 additions & 98 deletions python/llm/src/ipex_llm/transformers/models/internlm.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -36,18 +36,16 @@
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch InternLM model."""
import math
from typing import Optional, Tuple, List

import torch
import torch.utils.checkpoint
from torch import nn
from ipex_llm.utils.common.log4Error import invalidInputError
from ipex_llm.transformers.models.common import merge_qkv_base, attention_softmax
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 should_use_fuse_rope, apply_rotary_pos_emb
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 update_past_key_value
from ipex_llm.transformers.models.utils import use_sdp, use_sdp_causal
from einops import rearrange


Expand Down Expand Up @@ -98,35 +96,10 @@ def internlm_attention_forward(

# IPEX-LLM OPT: sdp
attn_weights = None
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)
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 Expand Up @@ -207,38 +180,10 @@ def internlm2_attention_forward(

# IPEX-LLM OPT: sdp
attn_weights = None
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)
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 = nn.functional.softmax(attn_weights,
dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_output = torch.matmul(attn_weights, value_states)
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 Expand Up @@ -409,38 +354,11 @@ def internlm_xcomposser2_attention_forward(
past_key_value = (key_states, value_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)
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 = nn.functional.softmax(attn_weights,
dim=-1, dtype=torch.float32).to(query_states.dtype)
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
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