ayo/ipex-llm
1
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 1

Refactor qwen2 moe (#11244)

Browse source
This commit is contained in:
Yishuo Wang 2024-06-07 13:14:54 +08:00 committed by GitHub
parent 7b753dc8ca
commit ef8e9b2ecd
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
2 changed files with 40 additions and 475 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

10
python/llm/src/ipex_llm/transformers/convert.py
View file

@ -713,6 +713,9 @@ def _optimize_pre(model):
if model.config.model_type == "qwen2":
from ipex_llm.transformers.models.qwen2 import merge_qkv
model.apply(merge_qkv)
if model.config.model_type == "qwen2_moe":
from ipex_llm.transformers.models.qwen2_moe import merge_qkv
model.apply(merge_qkv)
if model.config.model_type == "stablelm":
# For stablelm-zephyr-3b and stablelm-2-zephyr-1_6b
from ipex_llm.transformers.models.stablelm import merge_qkv
@ -1305,8 +1308,8 @@ def _optimize_post(model, lightweight_bmm=False):
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)
from ipex_llm.transformers.models.qwen2_moe import qwen2moe_moeblock_forward
from ipex_llm.transformers.models.qwen2_moe import qwen2moe_attention_forward
from ipex_llm.transformers.models.qwen2_moe import qwen2moe_model_forward
from ipex_llm.transformers.models.qwen2 import qwen2_attention_forward
convert_forward(model,
module.Qwen2MoeModel,
qwen2moe_model_forward)
@ -1321,7 +1324,10 @@ def _optimize_post(model, lightweight_bmm=False):
llama_mlp_forward)
convert_forward(model,
module.Qwen2MoeAttention,
qwen2moe_attention_forward)
qwen2_attention_forward)
convert_forward(model,
module.Qwen2MoeSdpaAttention,
qwen2_attention_forward)
elif model.config.model_type == "cohere":
# for CohereForAI/c4ai-command-r-v01
modeling_module_name = model.__class__.__module__

505
python/llm/src/ipex_llm/transformers/models/qwen2_moe.py
View file

@ -37,39 +37,20 @@
# limitations under the License.
""" PyTorch Qwen2MoE model."""
import math
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.checkpoint
import warnings
from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List
from ipex_llm.transformers.models.llama import repeat_kv
from ipex_llm.transformers.models.utils import should_use_fuse_rope
from ipex_llm.transformers.models.utils import extend_kv_cache, append_kv_cache
from ipex_llm.transformers.models.utils import apply_rotary_pos_emb_cache_freq_xpu
from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_36
from transformers.models.qwen2.modeling_qwen2 import apply_rotary_pos_emb
from typing import Optional, Tuple, Union, List
from ipex_llm.utils.common import invalidInputError
from ipex_llm.transformers.models.utils import decoding_fast_path_qtype_check
from ipex_llm.transformers.models.utils import use_flash_attention
from transformers.models.qwen2_moe.modeling_qwen2_moe import Qwen2MoeModel, apply_rotary_pos_emb
from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
from ipex_llm.transformers.kv import DynamicFp8Cache
from ipex_llm.transformers.models.utils import use_quantize_kv_cache
from ipex_llm.transformers.kv import DynamicFp8Cache, DynamicNormalCache
import os
KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
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
from transformers.models.qwen2_moe.modeling_qwen2_moe import (
_prepare_4d_causal_attention_mask_for_sdpa, _prepare_4d_causal_attention_mask,
Qwen2MoeAttention,
)
from transformers.modeling_outputs import MoeModelOutputWithPast
try:
from transformers.cache_utils import Cache, DynamicCache
except ImportError:
Cache = Tuple[torch.Tensor]
import logging
from transformers.cache_utils import Cache, DynamicCache
from transformers import logging
@ -90,9 +71,12 @@ def qwen2moe_model_forward(
return_dict: Optional[bool] = None,
):
use_cache = use_cache if use_cache is not None else self.config.use_cache
if use_cache and use_quantize_kv_cache(self.layers[0].mlp.shared_expert.up_proj, input_ids):
if not isinstance(past_key_values, DynamicFp8Cache):
use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.shared_expert.up_proj, input_ids)
if use_cache:
if use_quantize_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 isinstance(past_key_values, DynamicNormalCache):
past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
return qwen2_moe_model_forward_internal(
self=self,
input_ids=input_ids,
@ -290,452 +274,27 @@ def qwen2_moe_model_forward_internal(
)
def qwen2moe_attention_forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if use_quantize_kv_cache(self.q_proj, hidden_states):
forward_function = qwen2moe_attention_forward_quantized
elif hidden_states.device.type == "cpu":
forward_function = qwen2moe_attention_forward_sdpa
else:
forward_function = qwen2moe_attention_forward_origin
return forward_function(
self=self,
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
**kwargs,
)
def merge_qkv(module: torch.nn.Module):
if isinstance(module, Qwen2MoeAttention):
new_weight = torch.cat([
module.q_proj.weight.data,
module.k_proj.weight.data,
module.v_proj.weight.data,
], dim=0)
new_bias = torch.cat([
module.q_proj.bias.data,
module.k_proj.bias.data,
module.v_proj.bias.data,
], dim=-1)
qkv_proj = torch.nn.Linear(0, 0, bias=True)
qkv_proj.weight = torch.nn.Parameter(new_weight, requires_grad=False)
qkv_proj.bias = torch.nn.Parameter(new_bias, requires_grad=False)
qkv_proj.in_features = new_weight.size(1)
qkv_proj.out_features = new_weight.size(0)
module.qkv_proj = qkv_proj
def qwen2moe_attention_forward_quantized(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
**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.`"
)
use_fuse_rope = should_use_fuse_rope(hidden_states, position_ids, self.training)
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len,
self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len,
self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len,
self.num_key_value_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
invalidInputError(self.layer_idx is not None,
"The cache structure has changed since version v4.36. "
f"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(value_states, seq_len=kv_seq_len)
if use_fuse_rope:
query_states, key_states = apply_rotary_pos_emb_cache_freq_xpu(query_states, key_states,
sin, cos, "qwen2_moe",
position_ids)
else:
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
cos, sin, position_ids)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
key_states, value_states = past_key_value.update(key_states, value_states,
self.layer_idx, cache_kwargs)
if q_len == 1 and query_states.device.type == 'xpu' and not self.training \
and not hidden_states.requires_grad:
import xe_addons
attn_weights = xe_addons.query_key_fp8_matmul(query_states, key_states)
else:
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))
attn_weights = attn_weights / math.sqrt(self.head_dim)
invalidInputError(attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len),
("Attention weights should be of size "
f"{(bsz, self.num_heads, q_len, kv_seq_len)},"
"but is {attn_weights.size()}"))
if attention_mask is not None:
invalidInputError(attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
(f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}"
f" but is {attention_mask.size()}"))
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_weights = nn.functional.dropout(attn_weights,
p=self.attention_dropout, training=self.training)
if q_len == 1 and query_states.device.type == 'xpu' and not self.training \
and not hidden_states.requires_grad:
import xe_addons
attn_output = xe_addons.attn_value_fp8_matmul(attn_weights, value_states)
else:
attn_output = torch.matmul(attn_weights, value_states)
invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
"`attn_output` should be of size "
f"{(bsz, self.num_heads, q_len, self.head_dim)},"
f" but is {attn_output.size()}")
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
def qwen2moe_attention_forward_origin(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
use_fuse_rope = should_use_fuse_rope(hidden_states, position_ids, self.training)
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()
device = hidden_states.device
enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
qtype_check = decoding_fast_path_qtype_check(self.q_proj)
decoding_fast_path = (qtype_check and use_fuse_rope
and enough_kv_room and bsz * q_len == 1)
decoding_fast_path = decoding_fast_path and not self.q_proj.enable_xetla
if decoding_fast_path:
hidden_states = hidden_states.view(1, -1)
cache_k = past_key_value.key_cache[self.layer_idx]
cache_v = past_key_value.value_cache[self.layer_idx]
kv_seq_len = cache_k.shape[-2]
import xe_linear
args = [hidden_states, self.q_proj.weight, self.k_proj.weight, self.v_proj.weight,
self.q_proj.bias, self.k_proj.bias, self.v_proj.bias, position_ids, cache_k,
cache_v, self.q_proj.weight.qtype, self.v_proj.weight.qtype, kv_seq_len,
self.head_dim, self.rotary_emb.base]
query_states, key_states, value_states = xe_linear.forward_qkv_bias(*args)
kv_seq_len += 1
if self.layer_idx == 0:
past_key_value._seen_tokens = kv_seq_len
past_key_value.key_cache[self.layer_idx] = key_states
past_key_value.value_cache[self.layer_idx] = value_states
else:
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len,
self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len,
self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len,
self.num_key_value_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
if self.layer_idx is None:
invalidInputError(
False,
"The cache structure has changed since version v4.36. "
f"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(value_states, seq_len=kv_seq_len)
if use_fuse_rope:
query_states, key_states = apply_rotary_pos_emb_cache_freq_xpu(query_states, key_states,
sin, cos, "qwen2_moe",
position_ids)
else:
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
cos, sin, position_ids)
if past_key_value is not None:
if self.layer_idx == 0:
past_key_value._seen_tokens += key_states.shape[-2]
if len(past_key_value.key_cache) <= self.layer_idx:
past_key_value.key_cache.append(key_states)
past_key_value.value_cache.append(value_states)
else:
cache_k = past_key_value.key_cache[self.layer_idx]
cache_v = past_key_value.value_cache[self.layer_idx]
if not enough_kv_room:
# allocate new
new_c_k, new_c_v = extend_kv_cache(bsz,
self.num_key_value_heads, # Support GQA
self.head_dim,
cache_k.size(2),
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
dtype=cache_k.dtype,
device=device)
new_c_k[:] = cache_k
new_c_v[:] = cache_v
cache_k = new_c_k
cache_v = new_c_v
key_states, value_states = append_kv_cache(cache_k,
cache_v,
key_states,
value_states)
# update past_key_value
past_key_value.key_cache[self.layer_idx] = key_states
past_key_value.value_cache[self.layer_idx] = value_states
# 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)
if not self.training and not hidden_states.requires_grad and \
use_flash_attention(query_states, key_states, attention_mask):
attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
key_states.to(device, dtype=torch.float16),
value_states.to(device, dtype=torch.float16),
is_causal=True)
attn_weights = None
else:
attn_weights = torch.matmul(query_states,
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
invalidInputError(attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len),
("Attention weights should be of size "
f"{(bsz, self.num_heads, q_len, kv_seq_len)},"
"but is {attn_weights.size()}"))
if attention_mask is not None:
invalidInputError(attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
(f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}"
f" but is {attention_mask.size()}"))
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_weights = nn.functional.dropout(attn_weights,
p=self.attention_dropout, training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
"`attn_output` should be of size "
f"{(bsz, self.num_heads, q_len, self.head_dim)},"
f" but is {attn_output.size()}")
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output.to(hidden_states.dtype), attn_weights, past_key_value
def qwen2moe_attention_forward_sdpa(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
use_fuse_rope = should_use_fuse_rope(hidden_states, position_ids, self.training)
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()
device = hidden_states.device
enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
qtype_check = decoding_fast_path_qtype_check(self.q_proj)
decoding_fast_path = (qtype_check and use_fuse_rope
and enough_kv_room and bsz * q_len == 1)
decoding_fast_path = decoding_fast_path and not self.q_proj.enable_xetla
if decoding_fast_path:
hidden_states = hidden_states.view(1, -1)
cache_k = past_key_value.key_cache[self.layer_idx]
cache_v = past_key_value.value_cache[self.layer_idx]
kv_seq_len = cache_k.shape[-2]
import xe_linear
args = [hidden_states, self.q_proj.weight, self.k_proj.weight, self.v_proj.weight,
self.q_proj.bias, self.k_proj.bias, self.v_proj.bias, position_ids, cache_k,
cache_v, self.q_proj.weight.qtype, self.v_proj.weight.qtype, kv_seq_len,
self.head_dim, self.rotary_emb.base]
query_states, key_states, value_states = xe_linear.forward_qkv_bias(*args)
kv_seq_len += 1
if self.layer_idx == 0:
past_key_value._seen_tokens = kv_seq_len
past_key_value.key_cache[self.layer_idx] = key_states
past_key_value.value_cache[self.layer_idx] = value_states
else:
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len,
self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len,
self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len,
self.num_key_value_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
if self.layer_idx is None:
invalidInputError(
False,
"The cache structure has changed since version v4.36. "
f"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(value_states, seq_len=kv_seq_len)
if use_fuse_rope:
query_states, key_states = apply_rotary_pos_emb_cache_freq_xpu(query_states, key_states,
sin, cos, "qwen2_moe",
position_ids)
else:
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
cos, sin, position_ids)
if past_key_value is not None:
if self.layer_idx == 0:
past_key_value._seen_tokens += key_states.shape[-2]
if len(past_key_value.key_cache) <= self.layer_idx:
past_key_value.key_cache.append(key_states)
past_key_value.value_cache.append(value_states)
else:
cache_k = past_key_value.key_cache[self.layer_idx]
cache_v = past_key_value.value_cache[self.layer_idx]
if not enough_kv_room:
# allocate new
new_c_k, new_c_v = extend_kv_cache(bsz,
self.num_key_value_heads, # Support GQA
self.head_dim,
cache_k.size(2),
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
dtype=cache_k.dtype,
device=device)
new_c_k[:] = cache_k
new_c_v[:] = cache_v
cache_k = new_c_k
cache_v = new_c_v
key_states, value_states = append_kv_cache(cache_k,
cache_v,
key_states,
value_states)
# update past_key_value
past_key_value.key_cache[self.layer_idx] = key_states
past_key_value.value_cache[self.layer_idx] = value_states
# 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)
if output_attentions:
attn_weights = torch.matmul(query_states,
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
invalidInputError(attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len),
("Attention weights should be of size "
f"{(bsz, self.num_heads, q_len, kv_seq_len)},"
"but is {attn_weights.size()}"))
if attention_mask is not None:
invalidInputError(attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
(f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}"
f" but is {attention_mask.size()}"))
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_weights = nn.functional.dropout(attn_weights,
p=self.attention_dropout, training=self.training)
else:
attn_weights = None
from torch.nn.functional import scaled_dot_product_attention as sdpa
attn_output = sdpa(query_states,
key_states,
value_states,
attn_mask=attention_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=self.is_causal and attention_mask is None and q_len > 1)
invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
"`attn_output` should be of size "
f"{(bsz, self.num_heads, q_len, self.head_dim)},"
f" but is {attn_output.size()}")
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights, past_key_value
del module.q_proj, module.k_proj, module.v_proj
def qwen2moe_moeblock_forward(self, hidden_states: torch.Tensor):