wehub-resource-sync
530 lines
18 KiB
Python
530 lines
18 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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import unittest
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import numpy as np
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from op_test import OpTest, get_cuda_version, get_device_place, is_custom_device
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import paddle
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import paddle.nn.functional as F
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from paddle import base
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from paddle.base import core
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def masked_fill(x):
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row, col = x.shape[0], x.shape[1]
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for i in range(row):
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for j in range(col):
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if x[i][j] == 0:
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x[i][j] = float('-inf')
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return x
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def init_mask(x):
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row, col = x.shape[0], x.shape[1]
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for i in range(row):
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for j in range(col):
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if x[i][j] == 0 and (j < 0.8 * col):
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x[i][j] = 1
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return x
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def softmax(x, kp_mask=None, attn_mask=None, bsz=None):
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if kp_mask is None and attn_mask is None:
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max = np.max(x, axis=1, keepdims=True)
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e_x = np.exp(x - max)
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sum = np.sum(e_x, axis=1, keepdims=True)
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f_x = e_x / sum
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return f_x
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else:
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# kp_mask
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current_kp_mask = kp_mask[bsz]
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row = current_kp_mask.shape[0]
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current_kp_mask = np.expand_dims(current_kp_mask, 0).repeat(row, axis=0)
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# attn_mask
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current_attn_mask = copy.deepcopy(attn_mask)
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current_attn_mask = masked_fill(current_attn_mask)
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current_kp_mask = masked_fill(current_kp_mask)
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x = x + current_kp_mask
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x = x + current_attn_mask
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max = np.max(x, axis=1, keepdims=True)
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e_x = np.exp(x - max)
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sum = np.sum(e_x, axis=1, keepdims=True)
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f_x = e_x / sum
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return f_x
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def get_csr_value(mat, layout, nnz):
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row, col = mat.shape[0], mat.shape[1]
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value = np.zeros(nnz)
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ptr = 0
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for i in range(row):
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for j in range(col):
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if layout[i][j] == 1:
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value[ptr] = mat[i][j]
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ptr += 1
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return value
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def ref_sparse_attention(
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q, k, v, offset, columns, kp_mask=None, attn_mask=None, bsz=None
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):
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row, col, nnz = q.shape[0], q.shape[1], columns.shape[0]
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mat = np.zeros((row, row))
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for cur_row in range(row):
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start_ptr = int(offset[cur_row])
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end_ptr = int(offset[cur_row + 1])
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for ptr in range(start_ptr, end_ptr):
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cur_col = int(columns[ptr])
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mat[cur_row][cur_col] = 1
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a = np.dot(q, k.T) * mat
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a_value = get_csr_value(a, mat, nnz)
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scaling = float(col) ** -0.5
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a = scaling * a
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for i in range(row):
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for j in range(row):
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if mat[i][j] == 0:
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a[i][j] = float('-inf')
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# softmax
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if kp_mask is None and attn_mask is None:
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b = softmax(a)
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else:
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b = softmax(a, kp_mask=kp_mask, attn_mask=attn_mask, bsz=bsz)
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b_value = get_csr_value(b, mat, nnz)
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result = np.dot(b, v)
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return result, a_value, b_value
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def ref_batch_sparse_attention(
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q, k, v, offset, columns, kp_mask=None, attn_mask=None
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):
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batch_size, num_heads, row, col = q.shape
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nnz = columns.shape[2]
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result = np.zeros((batch_size, num_heads, row, col))
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result_sdd = np.zeros((batch_size, num_heads, nnz))
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result_softmax = np.zeros((batch_size, num_heads, nnz))
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for i in range(batch_size):
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for j in range(num_heads):
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(
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cur_q,
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cur_k,
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cur_v,
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) = (
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q[i][j],
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k[i][j],
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v[i][j],
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)
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cur_offset, cur_columns = offset[i][j], columns[i][j]
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if kp_mask is None and attn_mask is None:
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cur_result, cur_sdd, cur_softmax = ref_sparse_attention(
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cur_q, cur_k, cur_v, cur_offset, cur_columns
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)
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else:
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cur_result, cur_sdd, cur_softmax = ref_sparse_attention(
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cur_q,
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cur_k,
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cur_v,
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cur_offset,
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cur_columns,
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kp_mask=kp_mask,
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attn_mask=attn_mask,
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bsz=i,
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)
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result[i][j] = cur_result
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result_sdd[i][j], result_softmax[i][j] = cur_sdd, cur_softmax
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return result, result_sdd, result_softmax
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def init_csr_format(batch_size, num_heads, rows, blocksize):
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block_num, block_last = rows / blocksize, rows % blocksize
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nnz_num = block_num * blocksize * blocksize + block_last * block_last
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offset = np.zeros(rows + 1)
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columns = np.zeros(int(nnz_num))
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mat = np.zeros((rows, rows))
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for i in range(0, rows, blocksize):
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for x in range(blocksize):
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for y in range(blocksize):
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p_x, p_y = i + x, i + y
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if (p_x < rows) and (p_y < rows):
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mat[p_x][p_y] = 1
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p_offset, p_column, count = 0, 0, 0
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for i in range(rows):
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for j in range(rows):
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if mat[i][j] != 0:
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count += 1
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columns[p_column] = j
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p_column += 1
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p_offset += 1
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offset[p_offset] = count
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offset = np.expand_dims(np.expand_dims(offset, 0), 0)
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offset = offset.repeat(num_heads, axis=1)
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offset = offset.repeat(batch_size, axis=0)
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columns = np.expand_dims(np.expand_dims(columns, 0), 0)
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columns = columns.repeat(num_heads, axis=1)
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columns = columns.repeat(batch_size, axis=0)
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return offset, columns
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def api_wrapper(
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q, k, v, offset, columns, key_padding_mask=None, attn_mask=None
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):
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return paddle._C_ops.sparse_attention(
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q, k, v, offset, columns, key_padding_mask, attn_mask
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)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or get_cuda_version() < 11030,
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"core is not compiled with CUDA and cuda version need larger than or equal to 11.3",
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)
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class TestSparseAttentionOp(OpTest):
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def config(self):
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self.shape = (1, 1, 16, 16)
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self.blocksize = 4
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self.dtype = "float64"
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self.use_mask = True
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def setUp(self):
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paddle.enable_static()
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self.config()
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self.op_type = "sparse_attention"
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self.python_api = api_wrapper
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self.python_out_sig = ['Out']
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self.place = get_device_place()
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self.q = np.random.random(self.shape).astype(self.dtype)
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self.k = np.random.random(self.shape).astype(self.dtype)
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self.v = np.random.random(self.shape).astype(self.dtype)
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# init CSR tensor
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offset, columns = init_csr_format(
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self.shape[0], self.shape[1], self.shape[2], self.blocksize
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)
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self.offset = offset.astype('int32')
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self.columns = columns.astype('int32')
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# init mask tensor
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key_padding_mask_shape = (self.shape[0], self.shape[2])
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attn_mask_shape = (self.shape[2], self.shape[2])
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key_padding_mask = np.random.randint(0, 2, size=key_padding_mask_shape)
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attn_mask = np.random.randint(0, 2, size=attn_mask_shape)
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key_padding_mask = init_mask(key_padding_mask)
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attn_mask = init_mask(attn_mask)
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self.key_padding_mask = key_padding_mask.astype(self.dtype)
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self.attn_mask = attn_mask.astype(self.dtype)
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if self.use_mask:
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result, result_sdd, result_softmax = ref_batch_sparse_attention(
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self.q,
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self.k,
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self.v,
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self.offset,
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self.columns,
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kp_mask=self.key_padding_mask,
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attn_mask=self.attn_mask,
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)
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else:
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result, result_sdd, result_softmax = ref_batch_sparse_attention(
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self.q, self.k, self.v, self.offset, self.columns
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)
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if self.use_mask:
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self.inputs = {
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'Q': self.q,
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'K': self.k,
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'V': self.v,
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'Offset': self.offset,
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'Columns': self.columns,
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'KeyPaddingMask': self.key_padding_mask,
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'AttnMask': self.attn_mask,
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}
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else:
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self.inputs = {
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'Q': self.q,
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'K': self.k,
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'V': self.v,
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'Offset': self.offset,
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'Columns': self.columns,
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}
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self.outputs = {
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'Out': result.astype(self.dtype),
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'SparseDotSdd': result_sdd.astype(self.dtype),
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'Softmax': result_softmax.astype(self.dtype),
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}
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def test_check_output(self):
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self.check_output_with_place(self.place)
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def test_check_grad(self):
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self.check_grad_with_place(self.place, ['Q'], 'Out')
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self.check_grad_with_place(self.place, ['K'], 'Out')
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self.check_grad_with_place(self.place, ['V'], 'Out')
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class TestSparseAttentionOpFp32Test(TestSparseAttentionOp):
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def config(self):
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self.shape = (1, 1, 8, 16)
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self.blocksize = 2
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self.dtype = "float32"
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self.use_mask = False
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class TestSparseAttentionOpShapeTest(TestSparseAttentionOp):
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def config(self):
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self.shape = (2, 2, 32, 8)
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self.blocksize = 8
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self.dtype = "float64"
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self.use_mask = False
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or get_cuda_version() < 11030,
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"core is not compiled with CUDA and cuda version need larger than or equal to 11.3",
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)
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class TestSparseAttentionAPI(unittest.TestCase):
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def setUp(self):
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self.place = get_device_place()
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self.shape = (1, 1, 8, 4)
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self.blocksize = 2
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self.dtype = 'float64'
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self.use_mask = True
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def test_static_graph(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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Q = paddle.static.data(name="Q", shape=self.shape, dtype=self.dtype)
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K = paddle.static.data(name="K", shape=self.shape, dtype=self.dtype)
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V = paddle.static.data(name="V", shape=self.shape, dtype=self.dtype)
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batch_size, num_heads, rows = (
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self.shape[0],
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self.shape[1],
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self.shape[2],
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)
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block_num = rows / self.blocksize
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block_last = rows % self.blocksize
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sparse_nnz_num = (
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block_num * self.blocksize * self.blocksize
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+ block_last * block_last
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)
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offset_shape = (batch_size, num_heads, rows + 1)
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columns_shape = (batch_size, num_heads, int(sparse_nnz_num))
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offset = paddle.static.data(
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name="Offset", shape=offset_shape, dtype="int32"
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)
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columns = paddle.static.data(
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name="Columns", shape=columns_shape, dtype="int32"
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)
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key_padding_mask_shape = (self.shape[0], self.shape[2])
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attn_mask_shape = (self.shape[2], self.shape[2])
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if self.use_mask:
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key_padding_mask = paddle.static.data(
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name="KeyPaddingMask",
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shape=key_padding_mask_shape,
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dtype=self.dtype,
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)
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attn_mask = paddle.static.data(
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name="AttnMask", shape=attn_mask_shape, dtype=self.dtype
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)
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Out = F.sparse_attention(
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Q,
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K,
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V,
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offset,
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columns,
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key_padding_mask=key_padding_mask,
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attn_mask=attn_mask,
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)
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else:
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Out = F.sparse_attention(Q, K, V, offset, columns)
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Q_np = np.random.random(self.shape).astype(self.dtype)
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K_np = np.random.random(self.shape).astype(self.dtype)
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V_np = np.random.random(self.shape).astype(self.dtype)
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offset_np, columns_np = init_csr_format(
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self.shape[0], self.shape[1], self.shape[2], self.blocksize
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)
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offset_np = offset_np.astype('int32')
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columns_np = columns_np.astype('int32')
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# init mask tensor
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key_padding_mask_np = np.random.randint(
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0, 2, size=key_padding_mask_shape
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)
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attn_mask_np = np.random.randint(0, 2, size=attn_mask_shape)
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key_padding_mask_np = init_mask(key_padding_mask_np)
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attn_mask_np = init_mask(attn_mask_np)
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key_padding_mask_np = key_padding_mask_np.astype(self.dtype)
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attn_mask_np = attn_mask_np.astype(self.dtype)
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exe = base.Executor(self.place)
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if self.use_mask:
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fetches_result = exe.run(
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feed={
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"Q": Q_np,
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"K": K_np,
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"V": V_np,
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"Offset": offset_np,
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"Columns": columns_np,
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'KeyPaddingMask': key_padding_mask_np,
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'AttnMask': attn_mask_np,
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},
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fetch_list=[Out],
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)
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expected_result, __, __ = ref_batch_sparse_attention(
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Q_np,
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K_np,
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V_np,
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offset_np,
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columns_np,
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kp_mask=key_padding_mask_np,
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attn_mask=attn_mask_np,
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)
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else:
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fetches_result = exe.run(
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feed={
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"Q": Q_np,
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"K": K_np,
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"V": V_np,
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"Offset": offset_np,
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"Columns": columns_np,
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},
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fetch_list=[Out],
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)
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expected_result, __, __ = ref_batch_sparse_attention(
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Q_np, K_np, V_np, offset_np, columns_np
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)
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np.testing.assert_allclose(
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fetches_result[0], expected_result, rtol=1e-05, atol=1e-05
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)
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def test_dygraph(self):
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paddle.disable_static()
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offset, columns = init_csr_format(
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self.shape[0], self.shape[1], self.shape[2], self.blocksize
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)
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offset = offset.astype('int32')
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columns = columns.astype('int32')
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query = np.random.random(self.shape).astype(self.dtype)
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key = np.random.random(self.shape).astype(self.dtype)
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value = np.random.random(self.shape).astype(self.dtype)
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# init mask tensor
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key_padding_mask_shape = (self.shape[0], self.shape[2])
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attn_mask_shape = (self.shape[2], self.shape[2])
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key_padding_mask = np.random.randint(0, 2, size=key_padding_mask_shape)
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attn_mask = np.random.randint(0, 2, size=attn_mask_shape)
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key_padding_mask = init_mask(key_padding_mask)
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attn_mask = init_mask(attn_mask)
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key_padding_mask = key_padding_mask.astype(self.dtype)
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attn_mask = attn_mask.astype(self.dtype)
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paddle_query = paddle.to_tensor(query, place=self.place)
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paddle_key = paddle.to_tensor(key, place=self.place)
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paddle_value = paddle.to_tensor(value, place=self.place)
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paddle_offset = paddle.to_tensor(offset, place=self.place)
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paddle_columns = paddle.to_tensor(columns, place=self.place)
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paddle_kp_mask = paddle.to_tensor(key_padding_mask, place=self.place)
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paddle_attn_mask = paddle.to_tensor(attn_mask, place=self.place)
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if self.use_mask:
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paddle_result = F.sparse_attention(
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paddle_query,
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paddle_key,
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paddle_value,
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paddle_offset,
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paddle_columns,
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key_padding_mask=paddle_kp_mask,
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attn_mask=paddle_attn_mask,
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)
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numpy_result, __, __ = ref_batch_sparse_attention(
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query,
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key,
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value,
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offset,
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columns,
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kp_mask=key_padding_mask,
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attn_mask=attn_mask,
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)
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numpy_result = numpy_result.astype(self.dtype)
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else:
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paddle_result = F.sparse_attention(
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paddle_query,
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paddle_key,
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|
paddle_value,
|
|
paddle_offset,
|
|
paddle_columns,
|
|
)
|
|
|
|
numpy_result, __, __ = ref_batch_sparse_attention(
|
|
query, key, value, offset, columns
|
|
)
|
|
numpy_result = numpy_result.astype(self.dtype)
|
|
|
|
np.testing.assert_allclose(
|
|
paddle_result.numpy(), numpy_result, rtol=1e-05, atol=1e-05
|
|
)
|
|
|
|
|
|
class TestSparseAttentionAPITestFloat(TestSparseAttentionAPI):
|
|
def setUp(self):
|
|
self.place = get_device_place()
|
|
self.shape = (2, 2, 8, 4)
|
|
self.blocksize = 2
|
|
self.dtype = 'float32'
|
|
self.use_mask = False
|
|
|
|
|
|
class TestSparseAttentionAPITestShape1(TestSparseAttentionAPI):
|
|
def setUp(self):
|
|
self.place = get_device_place()
|
|
self.shape = (2, 2, 64, 32)
|
|
self.blocksize = 2
|
|
self.dtype = 'float64'
|
|
self.use_mask = False
|
|
|
|
|
|
class TestSparseAttentionAPITestShape2(TestSparseAttentionAPI):
|
|
def setUp(self):
|
|
self.place = get_device_place()
|
|
self.shape = (2, 1, 64, 32)
|
|
self.blocksize = 2
|
|
self.dtype = 'float64'
|
|
self.use_mask = False
|
|
|
|
|
|
class TestSparseAttentionAPITestShape3(TestSparseAttentionAPI):
|
|
def setUp(self):
|
|
self.place = get_device_place()
|
|
self.shape = (4, 4, 128, 32)
|
|
self.blocksize = 8
|
|
self.dtype = 'float64'
|
|
self.use_mask = False
|
|
|
|
|
|
class TestSparseAttentionAPITestShape4(TestSparseAttentionAPI):
|
|
def setUp(self):
|
|
self.place = get_device_place()
|
|
self.shape = (3, 3, 35, 15)
|
|
self.blocksize = 3
|
|
self.dtype = 'float64'
|
|
self.use_mask = False
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|