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import torch
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
# pylint: disable=protected-access, missing-function-docstring, line-too-long
original_torch_bmm = torch.bmm
def torch_bmm(input, mat2, *, out=None):
if input.dtype != mat2.dtype:
mat2 = mat2.to(input.dtype)
# ARC GPUs can't allocate more than 4GB to a single block, Slice it:
batch_size_attention, input_tokens, mat2_shape = (
input.shape[0],
input.shape[1],
mat2.shape[2],
)
block_multiply = input.element_size()
slice_block_size = input_tokens * mat2_shape / 1024 / 1024 * block_multiply
block_size = batch_size_attention * slice_block_size
split_slice_size = batch_size_attention
if block_size > 4:
do_split = True
# Find something divisible with the input_tokens
while (split_slice_size * slice_block_size) > 4:
split_slice_size = split_slice_size // 2
if split_slice_size <= 1:
split_slice_size = 1
break
else:
do_split = False
split_2_slice_size = input_tokens
if split_slice_size * slice_block_size > 4:
slice_block_size2 = split_slice_size * mat2_shape / 1024 / 1024 * block_multiply
do_split_2 = True
# Find something divisible with the input_tokens
while (split_2_slice_size * slice_block_size2) > 4:
split_2_slice_size = split_2_slice_size // 2
if split_2_slice_size <= 1:
split_2_slice_size = 1
break
else:
do_split_2 = False
if do_split:
hidden_states = torch.zeros(
input.shape[0],
input.shape[1],
mat2.shape[2],
device=input.device,
dtype=input.dtype,
)
for i in range(batch_size_attention // split_slice_size):
start_idx = i * split_slice_size
end_idx = (i + 1) * split_slice_size
if do_split_2:
for i2 in range(
input_tokens // split_2_slice_size
): # pylint: disable=invalid-name
start_idx_2 = i2 * split_2_slice_size
end_idx_2 = (i2 + 1) * split_2_slice_size
hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = (
original_torch_bmm(
input[start_idx:end_idx, start_idx_2:end_idx_2],
mat2[start_idx:end_idx, start_idx_2:end_idx_2],
out=out,
)
)
else:
hidden_states[start_idx:end_idx] = original_torch_bmm(
input[start_idx:end_idx], mat2[start_idx:end_idx], out=out
)
else:
return original_torch_bmm(input, mat2, out=out)
return hidden_states
original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
def scaled_dot_product_attention(
query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False
):
# ARC GPUs can't allocate more than 4GB to a single block, Slice it:
if len(query.shape) == 3:
batch_size_attention, query_tokens, shape_four = query.shape
shape_one = 1
no_shape_one = True
else:
shape_one, batch_size_attention, query_tokens, shape_four = query.shape
no_shape_one = False
block_multiply = query.element_size()
slice_block_size = (
shape_one * query_tokens * shape_four / 1024 / 1024 * block_multiply
)
block_size = batch_size_attention * slice_block_size
split_slice_size = batch_size_attention
if block_size > 4:
do_split = True
# Find something divisible with the shape_one
while (split_slice_size * slice_block_size) > 4:
split_slice_size = split_slice_size // 2
if split_slice_size <= 1:
split_slice_size = 1
break
else:
do_split = False
split_2_slice_size = query_tokens
if split_slice_size * slice_block_size > 4:
slice_block_size2 = (
shape_one * split_slice_size * shape_four / 1024 / 1024 * block_multiply
)
do_split_2 = True
# Find something divisible with the batch_size_attention
while (split_2_slice_size * slice_block_size2) > 4:
split_2_slice_size = split_2_slice_size // 2
if split_2_slice_size <= 1:
split_2_slice_size = 1
break
else:
do_split_2 = False
if do_split:
hidden_states = torch.zeros(query.shape, device=query.device, dtype=query.dtype)
for i in range(batch_size_attention // split_slice_size):
start_idx = i * split_slice_size
end_idx = (i + 1) * split_slice_size
if do_split_2:
for i2 in range(
query_tokens // split_2_slice_size
): # pylint: disable=invalid-name
start_idx_2 = i2 * split_2_slice_size
end_idx_2 = (i2 + 1) * split_2_slice_size
if no_shape_one:
hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = (
original_scaled_dot_product_attention(
query[start_idx:end_idx, start_idx_2:end_idx_2],
key[start_idx:end_idx, start_idx_2:end_idx_2],
value[start_idx:end_idx, start_idx_2:end_idx_2],
attn_mask=(
attn_mask[start_idx:end_idx, start_idx_2:end_idx_2]
if attn_mask is not None
else attn_mask
),
dropout_p=dropout_p,
is_causal=is_causal,
)
)
else:
hidden_states[:, start_idx:end_idx, start_idx_2:end_idx_2] = (
original_scaled_dot_product_attention(
query[:, start_idx:end_idx, start_idx_2:end_idx_2],
key[:, start_idx:end_idx, start_idx_2:end_idx_2],
value[:, start_idx:end_idx, start_idx_2:end_idx_2],
attn_mask=(
attn_mask[
:, start_idx:end_idx, start_idx_2:end_idx_2
]
if attn_mask is not None
else attn_mask
),
dropout_p=dropout_p,
is_causal=is_causal,
)
)
else:
if no_shape_one:
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,
)
)
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,
)
)
else:
return original_scaled_dot_product_attention(
query,
key,
value,
attn_mask=attn_mask,
dropout_p=dropout_p,
is_causal=is_causal,
)
return hidden_states
def attention_init():
# ARC GPUs can't allocate more than 4GB to a single block:
torch.bmm = torch_bmm
torch.nn.functional.scaled_dot_product_attention = scaled_dot_product_attention
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