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fix mistral for transformers>=4.39 (#11191)
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* fix mistral for transformers>=4.39
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@songhappy
songhappy authored Jun 18, 2024
1 parent 67a1e05 commit c44b194
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Showing 2 changed files with 244 additions and 9 deletions.
24 changes: 15 additions & 9 deletions python/llm/src/ipex_llm/transformers/convert.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1400,15 +1400,23 @@ def _optimize_post(model, lightweight_bmm=False):
module.MistralRMSNorm,
llama_rms_norm_forward)
else:
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)
if version.parse(trans_version) >= version.parse("4.36.0"):
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)
from ipex_llm.transformers.models.mistral import mistral_attention_forward_4_36
from ipex_llm.transformers.models.mistral import mistral_model_forward_4_36
convert_forward(model,
module.MistralAttention,
mistral_attention_forward_4_36
)
if version.parse(trans_version) >= version.parse("4.39.0"):
from ipex_llm.transformers.models.mistral import mistral_attention_forward_4_39
convert_forward(model,
module.MistralAttention,
mistral_attention_forward_4_39
)
else:
from ipex_llm.transformers.models.mistral import mistral_attention_forward_4_36

convert_forward(model,
module.MistralAttention,
mistral_attention_forward_4_36
)
convert_forward(model,
module.MistralModel,
mistral_model_forward_4_36
Expand All @@ -1420,8 +1428,6 @@ def _optimize_post(model, lightweight_bmm=False):
module.MistralMLP,
llama_mlp_forward)
else:
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)
from ipex_llm.transformers.models.mistral import mistral_attention_forward
convert_forward(model,
module.MistralAttention,
Expand Down
229 changes: 229 additions & 0 deletions python/llm/src/ipex_llm/transformers/models/mistral.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1074,3 +1074,232 @@ def mistral_attention_forward_4_36_original(
attn_weights = None

return attn_output.to(original_dtype), attn_weights, past_key_value


def mistral_attention_forward_4_39(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor]=None,
position_ids: Optional[torch.LongTensor]=None,
past_key_value: Optional[Cache]=None,
output_attentions: bool=False,
use_cache: bool=False,
**kwargs
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
if use_quantize_kv_cache(self.q_proj, hidden_states):
forward_function = mistral_attention_forward_4_36_quantized
else:
forward_function = mistral_attention_forward_4_39_original
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=kwargs
)


def mistral_attention_forward_4_39_original(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor]=None,
position_ids: Optional[torch.LongTensor]=None,
past_key_value: Optional[Cache]=None,
output_attentions: bool=False,
use_cache: bool=False,
**kwargs
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
bsz, q_len, hidden_size = hidden_states.size()
device = hidden_states.device
# for flash attention
original_dtype = hidden_states.dtype

use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
decoding_fast_path = use_decoding_fast_path(self.q_proj,
use_fuse_rope,
enough_kv_room,
bsz * q_len)
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
query_states, key_states, value_states = xe_linear.forward_qkv(hidden_states,
self.q_proj.weight,
self.k_proj.weight,
self.v_proj.weight,
position_ids,
cache_k, cache_v,
self.q_proj.weight.qtype,
self.v_proj.weight.qtype,
kv_seq_len,
self.head_dim)
kv_seq_len += 1

# update past_key_value's seem_tokens and kv caches.
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:
if should_use_xetla_mm_qkv(self, device):
if not hasattr(self, "qkv_proj_qweight"):
self.qkv_proj_qweight = fuse_qkv_weight_xetla(self.q_proj,
self.k_proj,
self.v_proj,
self.q_proj.qtype)
import xe_linear
q_out_len = self.q_proj.out_len
k_out_len = self.k_proj.out_len
v_out_len = self.v_proj.out_len
qkv_states = xe_linear.mm_xetla(hidden_states,
self.qkv_proj_qweight,
self.q_proj.qtype)
query_states = qkv_states[:, :, :q_out_len]
key_states = qkv_states[:, :, q_out_len:q_out_len + k_out_len]
value_states = qkv_states[:, :, q_out_len + k_out_len:]
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 decodingwith 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)

if use_fuse_rope:
query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
key_states,
position_ids,
"mistral")
else:
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
cos, sin, position_ids, "mistral")

if past_key_value is not None:
# update the number of seen tokens
if self.layer_idx == 0:
past_key_value._seen_tokens += key_states.shape[-2]

# reuse k, v, self_attention
# update `past_key_value` with `key_states` and `value_states` for layer `layer_idx`
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

if not self.training and not hidden_states.requires_grad:
fsdp_flag = use_flash_attention(query_states, key_states)
else:
fsdp_flag = False
if fsdp_flag:
attention_dtype = torch.float16 # use fp16 for flash attention
else:
attention_dtype = original_dtype

if fsdp_flag:
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, self.num_key_value_groups).to(device,
dtype=attention_dtype)
value_states = repeat_kv(value_states, self.num_key_value_groups).to(device,
dtype=attention_dtype)
attn_output = F.scaled_dot_product_attention(query_states.to(dtype=attention_dtype),
key_states,
value_states,
is_causal=True)
attn_weights = None
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
elif use_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
# new fp16 sdp doesn't require repeat_kv
import xe_addons
attn_output = xe_addons.sdp(query_states, key_states, value_states, attention_mask)
attn_output = attn_output.view(query_states.shape)
attn_weights = None
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
else:
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, self.num_key_value_groups).to(device,
dtype=attention_dtype)
value_states = repeat_kv(value_states, self.num_key_value_groups).to(device,
dtype=attention_dtype)
if should_split_qkv_tensor(query_states, bsz, self.num_heads,
q_len, kv_seq_len, output_attentions):
attn_output, attn_weights = compute_attn_outputs_weights_split_tensor(query_states,
key_states,
value_states,
bsz,
q_len,
kv_seq_len,
self.num_heads,
self.head_dim,
self.hidden_size,
attention_mask)
else:
attn_output, attn_weights = compute_attn_outputs_weights(query_states,
key_states,
value_states,
bsz,
q_len,
kv_seq_len,
self.num_heads,
self.head_dim,
self.hidden_size,
attention_mask)

attn_output = self.o_proj(attn_output)

if not output_attentions:
attn_weights = None

return attn_output.to(original_dtype), attn_weights, past_key_value

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