import torch # 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