Spaces:
Runtime error
Runtime error
| 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 = 2.4 if input.dtype == torch.float32 else 1.2 | |
| block_size = (batch_size_attention * input_tokens * mat2_shape) / 1024 * block_multiply #MB | |
| split_slice_size = batch_size_attention | |
| if block_size >= 4000: | |
| do_split = True | |
| #Find something divisible with the input_tokens | |
| while ((split_slice_size * input_tokens * mat2_shape) / 1024 * block_multiply) > 4000: | |
| split_slice_size = split_slice_size // 2 | |
| if split_slice_size <= 1: | |
| split_slice_size = 1 | |
| break | |
| else: | |
| do_split = False | |
| split_block_size = (split_slice_size * input_tokens * mat2_shape) / 1024 * block_multiply #MB | |
| split_2_slice_size = input_tokens | |
| if split_block_size >= 4000: | |
| do_split_2 = True | |
| #Find something divisible with the input_tokens | |
| while ((split_slice_size * split_2_slice_size * mat2_shape) / 1024 * block_multiply) > 4000: | |
| 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: | |
| shape_one, batch_size_attention, query_tokens, shape_four = query.shape | |
| block_multiply = 2.4 if query.dtype == torch.float32 else 1.2 | |
| block_size = (shape_one * batch_size_attention * query_tokens * shape_four) / 1024 * block_multiply #MB | |
| split_slice_size = batch_size_attention | |
| if block_size >= 4000: | |
| do_split = True | |
| #Find something divisible with the shape_one | |
| while ((shape_one * split_slice_size * query_tokens * shape_four) / 1024 * block_multiply) > 4000: | |
| split_slice_size = split_slice_size // 2 | |
| if split_slice_size <= 1: | |
| split_slice_size = 1 | |
| break | |
| else: | |
| do_split = False | |
| split_block_size = (shape_one * split_slice_size * query_tokens * shape_four) / 1024 * block_multiply #MB | |
| split_2_slice_size = query_tokens | |
| if split_block_size >= 4000: | |
| do_split_2 = True | |
| #Find something divisible with the batch_size_attention | |
| while ((shape_one * split_slice_size * split_2_slice_size * shape_four) / 1024 * block_multiply) > 4000: | |
| 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 | |
| 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 | |
| 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 |