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fixed attentions for MoE

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mrq 2024-08-27 17:02:42 -05:00
parent b7b99a25f1
commit d423bc03c2
1 changed files with 74 additions and 93 deletions
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137
vall_e/models/arch/mixtral.py
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@ -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
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_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,
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(
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_mask=causal_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=is_causal,
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.transpose(1, 2).contiguous()
attn_output = attn_output.view(bsz, q_len, self.hidden_size)
attn_output = attn_output.reshape(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)
causal_mask = attention_mask
if attention_mask is not None:
@ -235,14 +208,23 @@ class MixtralAttention_Adapted(MixtralAttention):
# 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"):
if self.mode in ["fused_attn"]:
attn_output = fused_attn_func(
query_states,
key_states,
value_states,
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=self.attention_dropout if self.training else 0.0,
dropout_p=dropout_rate,
is_causal=is_causal,
)
@ -252,4 +234,3 @@ class MixtralAttention_Adapted(MixtralAttention):
attn_output = self.o_proj(attn_output)
return attn_output, None, past_key_value
"""