external/llite/library/ipex/attention.py

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_32_bit(input, mat2, *, out=None):
    # 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
        split_2_slice_size = input_tokens
        if split_slice_size * slice_block_size > 4:
            slice_block_size_2 = 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_size_2) > 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
    else:
        do_split = 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_32_bit(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_three = query.shape
        shape_four = 1
    else:
        batch_size_attention, query_tokens, shape_three, shape_four = query.shape

    block_multiply = query.element_size()
    slice_block_size = query_tokens * shape_three * 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 batch_size_attention
        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
        split_2_slice_size = query_tokens
        if split_slice_size * slice_block_size > 4:
            slice_block_size_2 = split_slice_size * shape_three * shape_four / 1024 / 1024 * block_multiply
            do_split_2 = True
            # Find something divisible with the query_tokens
            while (split_2_slice_size * slice_block_size_2) > 4:
                split_2_slice_size = split_2_slice_size // 2
                if split_2_slice_size <= 1:
                    split_2_slice_size = 1
                    break
            split_3_slice_size = shape_three
            if split_2_slice_size * slice_block_size_2 > 4:
                slice_block_size_3 = split_slice_size * split_2_slice_size * shape_four / 1024 / 1024 * block_multiply
                do_split_3 = True
                # Find something divisible with the shape_three
                while (split_3_slice_size * slice_block_size_3) > 4:
                    split_3_slice_size = split_3_slice_size // 2
                    if split_3_slice_size <= 1:
                        split_3_slice_size = 1
                        break
            else:
                do_split_3 = False
        else:
            do_split_2 = False
    else:
        do_split = 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 do_split_3:
                        for i3 in range(shape_three // split_3_slice_size): # pylint: disable=invalid-name
                            start_idx_3 = i3 * split_3_slice_size
                            end_idx_3 = (i3 + 1) * split_3_slice_size
                            hidden_states[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] = original_scaled_dot_product_attention(
                                query[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3],
                                key[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3],
                                value[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3],
                                attn_mask=attn_mask[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] 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:
                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