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add sdp_causal for mistral 4.36 (#11686)

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* add sdp_causal for mistral

* fix

* update
This commit is contained in:
Ruonan Wang 2024-08-01 13:57:31 +03:00 committed by GitHub
parent 45c730ff39
commit 736a7ef72e
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2 changed files with 31 additions and 7 deletions
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15
python/llm/src/ipex_llm/transformers/models/llama.py
View file

@ -1143,7 +1143,16 @@ def llama_attention_forward_4_41_quantized(
if len(past_key_value.key_cache) <= self.layer_idx: if len(past_key_value.key_cache) <= self.layer_idx:
repeated_key_states = repeat_kv(key_states, self.num_key_value_groups) repeated_key_states = repeat_kv(key_states, self.num_key_value_groups)
repeated_value_states = repeat_kv(value_states, self.num_key_value_groups) repeated_value_states = repeat_kv(value_states, self.num_key_value_groups)
if should_split_qkv_tensor(query_states, bsz, self.num_heads, if use_cache:
cache_kwargs = None
key_states, value_states = past_key_value.update(key_states, value_states,
self.layer_idx, cache_kwargs)
if use_cache and use_sdp_causal(q_len, kv_seq_len, self.head_dim,
query_states, self.training):
import xe_addons
attn_output = xe_addons.sdp_fp8_causal(query_states, key_states,
value_states, attention_mask)
elif should_split_qkv_tensor(query_states, bsz, self.num_heads,
q_len, kv_seq_len, output_attentions): q_len, kv_seq_len, output_attentions):
attn_output, _ = native_sdp_split_qkv_tensor(query_states, repeated_key_states, attn_output, _ = native_sdp_split_qkv_tensor(query_states, repeated_key_states,
repeated_value_states, repeated_value_states,
@ -1184,10 +1193,6 @@ def llama_attention_forward_4_41_quantized(
attn_weights = nn.functional.softmax(attn_weights, dim=-1, attn_weights = nn.functional.softmax(attn_weights, dim=-1,
dtype=torch.float32).to(query_states.dtype) dtype=torch.float32).to(query_states.dtype)
attn_output = torch.matmul(attn_weights, repeated_value_states) attn_output = torch.matmul(attn_weights, repeated_value_states)
if use_cache:
cache_kwargs = None
key_states, value_states = past_key_value.update(key_states, value_states,
self.layer_idx, cache_kwargs)
else: else:
cache_kwargs = None # Specific to RoPE models cache_kwargs = None # Specific to RoPE models
key_states, value_states = past_key_value.update(key_states, value_states, key_states, value_states = past_key_value.update(key_states, value_states,

21
python/llm/src/ipex_llm/transformers/models/mistral.py
View file

@ -52,7 +52,8 @@ from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, \
from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_31, \ from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_31, \
is_enough_kv_cache_room_4_36 is_enough_kv_cache_room_4_36
from ipex_llm.transformers.low_bit_linear import SYM_INT4, FP8E5, IQ2_XXS from ipex_llm.transformers.low_bit_linear import SYM_INT4, FP8E5, IQ2_XXS
from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp, use_sdp_fp8 from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp, use_sdp_fp8, \
use_sdp_causal
from ipex_llm.transformers.models.utils import use_decoding_fast_path from ipex_llm.transformers.models.utils import use_decoding_fast_path
from ipex_llm.transformers.models.llama import llama_decoding_fast_path_qtype_check from ipex_llm.transformers.models.llama import llama_decoding_fast_path_qtype_check
from ipex_llm.transformers.models.llama import should_use_xetla_mm_qkv from ipex_llm.transformers.models.llama import should_use_xetla_mm_qkv
@ -599,6 +600,15 @@ def mistral_attention_forward_original(
attn_weights = None attn_weights = None
attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
elif use_sdp_causal(q_len, key_states.shape[2], self.head_dim,
query_states, self.training):
import xe_addons
attn_output = xe_addons.sdp_causal(query_states, key_states.contiguous(),
value_states.contiguous(), 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)
elif use_sdp(q_len, key_states.shape[2], self.head_dim, query_states): elif use_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
# new fp16 sdp doesn't require repeat_kv # new fp16 sdp doesn't require repeat_kv
import xe_addons import xe_addons
@ -1052,6 +1062,15 @@ def mistral_attention_forward_4_36_original(
attn_weights = None attn_weights = None
attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
elif use_sdp_causal(q_len, key_states.shape[2], self.head_dim,
query_states, self.training):
import xe_addons
attn_output = xe_addons.sdp_causal(query_states, key_states.contiguous(),
value_states.contiguous(), 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)
elif use_sdp(q_len, key_states.shape[2], self.head_dim, query_states): elif use_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
# new fp16 sdp doesn't require repeat_kv # new fp16 sdp doesn't require repeat_kv
import xe_addons import xe_addons