fixed attentions for MoE
This commit is contained in:
parent
b7b99a25f1
commit
d423bc03c2
|
|
@ -1,5 +1,6 @@
|
|||
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/mixtral/modeling_mixtral.py
|
||||
|
||||
import math
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from typing import Literal, overload, Optional, Tuple
|
||||
|
|
@ -13,6 +14,16 @@ try:
|
|||
except Exception as e:
|
||||
pass
|
||||
|
||||
try:
|
||||
from .llama import fused_attn_func
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
try:
|
||||
from .llama import memory_efficient_attention
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
# This is required because batch sizes > 1 throws errors
|
||||
def MixtralSparseMoeBlock_forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
""" """
|
||||
|
|
@ -99,6 +110,7 @@ class MixtralAttention_Adapted(MixtralAttention):
|
|||
use_cache=use_cache,
|
||||
)
|
||||
|
||||
dropout_rate = self.attention_dropout if self.training else 0.0
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
|
||||
query_states = self.q_proj(hidden_states)
|
||||
|
|
@ -127,98 +139,59 @@ class MixtralAttention_Adapted(MixtralAttention):
|
|||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||||
|
||||
causal_mask = attention_mask
|
||||
if attention_mask is not None: # no matter the length, we just slice it
|
||||
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
||||
if self.mode in ["xformers", "flash_attn"]:
|
||||
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
||||
# to be able to avoid many of these transpose/reshape/view.
|
||||
query_states = query_states.transpose(1, 2)
|
||||
key_states = key_states.transpose(1, 2)
|
||||
value_states = value_states.transpose(1, 2)
|
||||
|
||||
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
|
||||
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
||||
if query_states.device.type == "cuda" and attention_mask is not None:
|
||||
query_states = query_states.contiguous()
|
||||
key_states = key_states.contiguous()
|
||||
value_states = value_states.contiguous()
|
||||
"""
|
||||
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
||||
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
||||
# cast them back in the correct dtype just to be sure everything works as expected.
|
||||
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
||||
# in fp32. (LlamaRMSNorm handles it correctly)
|
||||
|
||||
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
|
||||
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
|
||||
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
|
||||
is_causal = True if causal_mask is None and q_len > 1 else False
|
||||
input_dtype = query_states.dtype
|
||||
if input_dtype == torch.float32:
|
||||
if torch.is_autocast_enabled():
|
||||
target_dtype = torch.get_autocast_gpu_dtype()
|
||||
# Handle the case where the model is quantized
|
||||
elif hasattr(self.config, "_pre_quantization_dtype"):
|
||||
target_dtype = self.config._pre_quantization_dtype
|
||||
else:
|
||||
target_dtype = self.q_proj.weight.dtype
|
||||
|
||||
#with torch.backends.cuda.sdp_kernel(enable_flash=self.mode == "flash", enable_math=self.mode == "math", enable_mem_efficient=self.mode == "mem_efficient"):
|
||||
if self.mode == "flash_attn":
|
||||
attn_output = flash_attn_func(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
causal=True,
|
||||
softmax_scale=None, # 1, / math.sqrt(cfg.head_dim),
|
||||
dropout_p=self.attention_dropout if self.training else 0.0,
|
||||
)
|
||||
else:
|
||||
with torch.nn.attention.sdpa_kernel(self.mode):
|
||||
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
||||
query_states = query_states.to(target_dtype)
|
||||
key_states = key_states.to(target_dtype)
|
||||
value_states = value_states.to(target_dtype)
|
||||
"""
|
||||
|
||||
if self.mode == "flash_attn":
|
||||
attn_output = flash_attn_func(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
attn_mask=causal_mask,
|
||||
dropout_p=self.attention_dropout if self.training else 0.0,
|
||||
is_causal=is_causal,
|
||||
causal=True,
|
||||
softmax_scale=1.0 / math.sqrt(self.head_dim),
|
||||
dropout_p=dropout_rate,
|
||||
)
|
||||
|
||||
attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
|
||||
elif self.mode == "xformers":
|
||||
attn_output = memory_efficient_attention(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
attn_bias = LowerTriangularMask() if attention_mask is None or attention_mask[0, 0, 0, 1] == 0 else None,
|
||||
scale = 1.0 / math.sqrt(self.head_dim),
|
||||
p=dropout_rate
|
||||
)
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
attn_output = attn_output.view(bsz, q_len, self.hidden_size)
|
||||
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
return attn_output, None, past_key_value
|
||||
"""
|
||||
def forward(
|
||||
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,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.45
|
||||
**kwargs,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
if output_attentions:
|
||||
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
|
||||
return super().forward(
|
||||
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,
|
||||
cache_position=cache_position,
|
||||
position_embeddings=position_embeddings,
|
||||
)
|
||||
|
||||
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)
|
||||
|
||||
if position_embeddings is None:
|
||||
cos, sin = self.rotary_emb(value_states, position_ids)
|
||||
else:
|
||||
cos, sin = position_embeddings
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
||||
|
||||
if past_key_value is not None:
|
||||
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
||||
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
||||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||||
|
||||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||||
attn_output = self.o_proj(attn_output)
|
||||
return attn_output, None, past_key_value
|
||||
|
||||
causal_mask = attention_mask
|
||||
if attention_mask is not None:
|
||||
|
|
@ -234,22 +207,30 @@ class MixtralAttention_Adapted(MixtralAttention):
|
|||
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
|
||||
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
|
||||
is_causal = True if causal_mask is None and q_len > 1 else False
|
||||
|
||||
#with torch.backends.cuda.sdp_kernel(enable_flash=self.mode == "flash", enable_math=self.mode == "math", enable_mem_efficient=self.mode == "mem_efficient"):
|
||||
with torch.nn.attention.sdpa_kernel(self.mode):
|
||||
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
||||
|
||||
if self.mode in ["fused_attn"]:
|
||||
attn_output = fused_attn_func(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
attn_mask=causal_mask,
|
||||
dropout_p=self.attention_dropout if self.training else 0.0,
|
||||
is_causal=is_causal,
|
||||
causal=True,
|
||||
softmax_scale=1.0 / math.sqrt(self.head_dim),
|
||||
dropout_p=dropout_rate,
|
||||
)
|
||||
else:
|
||||
with torch.nn.attention.sdpa_kernel(self.mode):
|
||||
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
attn_mask=causal_mask,
|
||||
dropout_p=dropout_rate,
|
||||
is_causal=is_causal,
|
||||
)
|
||||
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
attn_output = attn_output.view(bsz, q_len, -1)
|
||||
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
return attn_output, None, past_key_value
|
||||
"""
|
||||
return attn_output, None, past_key_value
|
||||
Loading…
Reference in New Issue
Block a user