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import os
import torch
from functools import cache, wraps
# pylint: disable=protected-access, missing-function-docstring, line-too-long
# ARC GPUs can't allocate more than 4GB to a single block so we slice the attention layers
sdpa_slice_trigger_rate = float(os.environ.get('IPEX_SDPA_SLICE_TRIGGER_RATE', 1))
attention_slice_rate = float(os.environ.get('IPEX_ATTENTION_SLICE_RATE', 0.5))
# Find something divisible with the input_tokens
@cache
def find_split_size(original_size, slice_block_size, slice_rate=2):
split_size = original_size
while True:
if (split_size * slice_block_size) <= slice_rate and original_size % split_size == 0:
return split_size
split_size = split_size - 1
if split_size <= 1:
return 1
return split_size
# Find slice sizes for SDPA
@cache
def find_sdpa_slice_sizes(query_shape, key_shape, query_element_size, slice_rate=2, trigger_rate=3):
batch_size, attn_heads, query_len, _ = query_shape
_, _, key_len, _ = key_shape
slice_batch_size = attn_heads * (query_len * key_len) * query_element_size / 1024 / 1024 / 1024
split_batch_size = batch_size
split_head_size = attn_heads
split_query_size = query_len
do_batch_split = False
do_head_split = False
do_query_split = False
if batch_size * slice_batch_size >= trigger_rate:
do_batch_split = True
split_batch_size = find_split_size(batch_size, slice_batch_size, slice_rate=slice_rate)
if split_batch_size * slice_batch_size > slice_rate:
slice_head_size = split_batch_size * (query_len * key_len) * query_element_size / 1024 / 1024 / 1024
do_head_split = True
split_head_size = find_split_size(attn_heads, slice_head_size, slice_rate=slice_rate)
if split_head_size * slice_head_size > slice_rate:
slice_query_size = split_batch_size * split_head_size * (key_len) * query_element_size / 1024 / 1024 / 1024
do_query_split = True
split_query_size = find_split_size(query_len, slice_query_size, slice_rate=slice_rate)
return do_batch_split, do_head_split, do_query_split, split_batch_size, split_head_size, split_query_size
original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
@wraps(torch.nn.functional.scaled_dot_product_attention)
def dynamic_scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, **kwargs):
if query.device.type != "xpu":
return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, **kwargs)
is_unsqueezed = False
if len(query.shape) == 3:
query = query.unsqueeze(0)
is_unsqueezed = True
if len(key.shape) == 3:
key = key.unsqueeze(0)
if len(value.shape) == 3:
value = value.unsqueeze(0)
do_batch_split, do_head_split, do_query_split, split_batch_size, split_head_size, split_query_size = find_sdpa_slice_sizes(query.shape, key.shape, query.element_size(), slice_rate=attention_slice_rate, trigger_rate=sdpa_slice_trigger_rate)
# Slice SDPA
if do_batch_split:
batch_size, attn_heads, query_len, _ = query.shape
_, _, _, head_dim = value.shape
hidden_states = torch.zeros((batch_size, attn_heads, query_len, head_dim), device=query.device, dtype=query.dtype)
if attn_mask is not None:
attn_mask = attn_mask.expand((query.shape[0], query.shape[1], query.shape[2], key.shape[-2]))
for ib in range(batch_size // split_batch_size):
start_idx = ib * split_batch_size
end_idx = (ib + 1) * split_batch_size
if do_head_split:
for ih in range(attn_heads // split_head_size): # pylint: disable=invalid-name
start_idx_h = ih * split_head_size
end_idx_h = (ih + 1) * split_head_size
if do_query_split:
for iq in range(query_len // split_query_size): # pylint: disable=invalid-name
start_idx_q = iq * split_query_size
end_idx_q = (iq + 1) * split_query_size
hidden_states[start_idx:end_idx, start_idx_h:end_idx_h, start_idx_q:end_idx_q, :] = original_scaled_dot_product_attention(
query[start_idx:end_idx, start_idx_h:end_idx_h, start_idx_q:end_idx_q, :],
key[start_idx:end_idx, start_idx_h:end_idx_h, :, :],
value[start_idx:end_idx, start_idx_h:end_idx_h, :, :],
attn_mask=attn_mask[start_idx:end_idx, start_idx_h:end_idx_h, start_idx_q:end_idx_q, :] if attn_mask is not None else attn_mask,
dropout_p=dropout_p, is_causal=is_causal, **kwargs
)
else:
hidden_states[start_idx:end_idx, start_idx_h:end_idx_h, :, :] = original_scaled_dot_product_attention(
query[start_idx:end_idx, start_idx_h:end_idx_h, :, :],
key[start_idx:end_idx, start_idx_h:end_idx_h, :, :],
value[start_idx:end_idx, start_idx_h:end_idx_h, :, :],
attn_mask=attn_mask[start_idx:end_idx, start_idx_h:end_idx_h, :, :] if attn_mask is not None else attn_mask,
dropout_p=dropout_p, is_causal=is_causal, **kwargs
)
else:
hidden_states[start_idx:end_idx, :, :, :] = original_scaled_dot_product_attention(
query[start_idx:end_idx, :, :, :],
key[start_idx:end_idx, :, :, :],
value[start_idx:end_idx, :, :, :],
attn_mask=attn_mask[start_idx:end_idx, :, :, :] if attn_mask is not None else attn_mask,
dropout_p=dropout_p, is_causal=is_causal, **kwargs
)
torch.xpu.synchronize(query.device)
else:
hidden_states = original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, **kwargs)
if is_unsqueezed:
hidden_states.squeeze(0)
return hidden_states
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