wehub-resource-sync
368 lines
12 KiB
Python
368 lines
12 KiB
Python
# Copyright (c) 2024 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 unittest
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import numpy as np
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from op_test import (
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OpTest,
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check_cudnn_version_and_compute_capability,
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get_device,
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is_custom_device,
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)
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import paddle
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import paddle.distributed as dist
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import paddle.nn.functional as F
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from paddle import _C_ops
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from paddle.base import core
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from paddle.distributed.auto_parallel.static.dist_attribute import (
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DistTensorSpec,
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TensorDistAttr,
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)
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from paddle.nn.functional import swiglu as fused_swiglu_impl
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def swiglu(x, y, out_grad):
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if isinstance(x, np.ndarray):
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x = paddle.to_tensor(x)
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y = paddle.to_tensor(y)
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out_grad = paddle.to_tensor(out_grad)
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origin_x = x.detach().clone()
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origin_x.stop_gradient = False
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x = origin_x
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origin_y = y.detach().clone()
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origin_y.stop_gradient = False
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y = origin_y
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dtype = x.dtype
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need_convert = False
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assert dtype == y.dtype
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output_dtype = dtype
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if paddle.is_compiled_with_cuda() or is_custom_device():
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if dtype in [paddle.float16, paddle.bfloat16]:
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output_dtype = paddle.float32
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x = x.astype(output_dtype)
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y = y.astype(output_dtype)
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need_convert = True
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out = F.silu(x) * y
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if need_convert:
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out = out.astype(dtype)
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out.backward(out_grad)
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ret = [
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out.astype(output_dtype),
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origin_x.grad.astype(output_dtype),
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origin_y.grad.astype(output_dtype),
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]
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return ret
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def fused_swiglu(x, y, out_grad, swiglu_func=fused_swiglu_impl):
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x = x.detach().clone()
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x.stop_gradient = False
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if y is not None:
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y = y.detach().clone()
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y.stop_gradient = False
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out = swiglu_func(x, y)
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out.backward(out_grad)
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output_dtype = x.dtype
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if paddle.is_compiled_with_cuda() or is_custom_device():
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if x.dtype in [paddle.float16, paddle.bfloat16]:
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output_dtype = paddle.float32
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ret = [
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out.astype(output_dtype),
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]
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if y is not None:
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x_grad, y_grad = x.grad, y.grad
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else:
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x_grad, y_grad = paddle.split(x.grad, 2, axis=-1)
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ret.append(x_grad.astype(output_dtype))
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ret.append(y_grad.astype(output_dtype))
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return ret
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tol_map = {
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paddle.float64: [1e-8, 1e-8],
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paddle.float32: [1e-6, 1e-6],
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paddle.float16: [1e-3, 1e-3],
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paddle.bfloat16: [1e-3, 1e-3],
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}
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class TestSwiGLUDygraph(unittest.TestCase):
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def fused_swiglu(self, x, y, out_grad):
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return fused_swiglu(x, y, out_grad)
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def fused_swiglu_impl(self, x, y=None):
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return fused_swiglu_impl(x, y)
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def check_dygraph_impl(self, device, shape, dtype):
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x = paddle.randn(shape, dtype=dtype)
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y = paddle.randn(shape, dtype=dtype)
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out_grad = paddle.randn(shape, dtype=dtype)
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ret1 = swiglu(x, y, out_grad)
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ret2 = self.fused_swiglu(x, y, out_grad)
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ret3 = self.fused_swiglu(paddle.concat([x, y], axis=-1), None, out_grad)
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atol, rtol = tol_map[dtype]
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err_msg = (
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f"Failed when device = {device}, dtype = {dtype}, shape = {shape}"
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)
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for t1, t2, t3 in zip(ret1, ret2, ret3):
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t1, t2, t3 = t1.numpy(), t2.numpy(), t3.numpy()
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np.testing.assert_allclose(
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t1, t2, atol=atol, rtol=rtol, err_msg=err_msg
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)
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np.testing.assert_equal(t2, t3, err_msg=err_msg)
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def check_dygraph(self, shape):
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metas = [('cpu', paddle.float32), ('cpu', paddle.float64)]
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if paddle.is_compiled_with_cuda() or is_custom_device():
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metas.append((get_device(), paddle.float32))
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metas.append((get_device(), paddle.float64))
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metas.append((get_device(), paddle.float16))
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if check_cudnn_version_and_compute_capability(
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min_device_capability=8
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):
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metas.append((get_device(), paddle.bfloat16))
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for device, dtype in metas:
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origin_device = paddle.get_device()
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paddle.set_device(device)
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for with_split in [True]:
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self.check_dygraph_impl(device, shape, dtype)
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paddle.set_device(origin_device)
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def check_static_graph(self, shape, dtype="float32"):
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x = paddle.static.data(name='x', shape=shape, dtype=dtype)
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y = paddle.static.data(name='y', shape=shape, dtype=dtype)
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concated_x = paddle.static.data(
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name='concated_x',
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shape=[*shape[:-1], shape[-1] * 2],
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dtype=dtype,
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)
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out1 = self.fused_swiglu_impl(x, y)
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out2 = self.fused_swiglu_impl(concated_x)
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concated_x_np = np.random.random(concated_x.shape).astype(dtype)
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x_np, y_np = np.split(concated_x_np, 2, axis=-1)
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exe = paddle.static.Executor()
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t1, t2 = exe.run(
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feed={'x': x_np, 'y': y_np, 'concated_x': concated_x_np},
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fetch_list=[out1, out2],
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)
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np.testing.assert_equal(t1, t2)
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def check_main(self, shape):
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self.check_dygraph(shape)
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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self.check_static_graph(shape)
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paddle.disable_static()
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def test_main(self):
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self.check_main([8, 100])
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self.check_main([4, 101])
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class TestSwigluOp(OpTest):
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def config(self):
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self.x_shape = (8, 128)
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self.check_auto_parallel = True
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def setUp(self):
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self.config()
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self.op_type = "swiglu"
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self.prim_op_type = "comp"
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self.python_api = fused_swiglu_impl
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self.public_python_api = fused_swiglu_impl
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x = np.random.uniform(-1, 1, self.x_shape).astype("float64")
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y = np.random.uniform(-1, 1, self.x_shape).astype("float64")
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out_grad = np.random.uniform(-1, 1, self.x_shape).astype("float64")
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res = swiglu(x, y, out_grad)
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self.inputs = {'x': x, 'y': y}
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self.outputs = {'out': res[0].numpy()}
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self.placements = {
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'x': [dist.Shard(1)],
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'y': [dist.Shard(1)],
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'out': [dist.Shard(1)],
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}
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def test_check_output(self):
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self.check_output(check_prim_pir=True)
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def test_check_grad(self):
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self.check_grad(
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['x', 'y'],
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'out',
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check_auto_parallel=self.check_auto_parallel,
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check_dygraph=1,
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check_prim_pir=True,
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)
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class TestSwigluOp2(TestSwigluOp):
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def setUp(self):
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self.config()
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self.op_type = "swiglu"
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self.prim_op_type = "comp"
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self.python_api = fused_swiglu_impl
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self.public_python_api = fused_swiglu_impl
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x = np.random.uniform(-1, 1, self.x_shape).astype("float64")
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tmp_inputs = np.split(x, 2, axis=-1)
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x = tmp_inputs[0]
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y = tmp_inputs[1]
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out_grad = np.random.uniform(-1, 1, x.shape).astype("float64")
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res = swiglu(x, y, out_grad)
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self.inputs = {'x': x, 'y': y}
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self.outputs = {'out': res[0].numpy()}
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self.placements = {
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'x': [dist.Shard(1)],
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'y': [dist.Shard(1)],
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'out': [dist.Shard(1)],
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}
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@unittest.skipIf(
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not (paddle.base.core.is_compiled_with_dist() or is_custom_device()),
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"The spmd rule is should be tested with distributed=ON",
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)
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class TestSwigluSpmd(unittest.TestCase):
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def setUp(self):
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self.kernel = 'swiglu'
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self.rule = paddle.base.core.get_phi_spmd_rule(self.kernel)
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x_shape = [64, 32]
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process_mesh = dist.ProcessMesh(mesh=[0, 1, 2, 3])
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x_tensor_dist_attr = TensorDistAttr()
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x_tensor_dist_attr.dims_mapping = [-1, 0]
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x_tensor_dist_attr.process_mesh = process_mesh
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self.x_dist_tensor_spec = DistTensorSpec(x_shape, x_tensor_dist_attr)
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self.y_dist_tensor_spec = DistTensorSpec(x_shape, x_tensor_dist_attr)
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self.out_dist_tensor_spec = DistTensorSpec(self.x_dist_tensor_spec)
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def test_input_x_y(self):
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec, self.y_dist_tensor_spec
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(len(result_dist_attrs), 2)
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self.assertEqual(len(inferred_input_dist_attrs), 2)
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self.assertEqual(len(inferred_output_dist_attrs), 1)
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self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [-1, 0])
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def test_input_x_unshard_last_dim(self):
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x_shape = [64, 32]
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process_mesh = dist.ProcessMesh(mesh=[0, 1, 2, 3])
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x_tensor_dist_attr = TensorDistAttr()
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x_tensor_dist_attr.dims_mapping = [0, -1]
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x_tensor_dist_attr.process_mesh = process_mesh
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self.x_dist_tensor_spec = DistTensorSpec(x_shape, x_tensor_dist_attr)
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec, DistTensorSpec()
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)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(len(result_dist_attrs), 2)
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self.assertEqual(len(inferred_input_dist_attrs), 2)
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self.assertEqual(len(inferred_output_dist_attrs), 1)
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self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [0, -1])
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"mamtul 0 size only with in cuda",
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)
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class TestSwiglu0SizeDygraph(unittest.TestCase):
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def test_swiglu(self):
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x = paddle.ones([0, 128], dtype="float32")
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y = paddle.ones([0, 128], dtype="float32")
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x.stop_gradient = False
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y.stop_gradient = False
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out = fused_swiglu_impl(x, y)
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dz = paddle.ones([0, 128], dtype="float32")
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out = _C_ops.swiglu_grad(x, y, dz)
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self.assertEqual(out[0].shape, x.shape)
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self.assertEqual(out[1].shape, y.shape)
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class TestSwigluOp_ZeroSize(OpTest):
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def config(self):
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self.x_shape = (0, 128)
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self.y_shape = (1, 128)
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self.out_shape = (0, 128)
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def setUp(self):
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self.config()
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self.op_type = "swiglu"
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self.python_api = fused_swiglu_impl
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self.public_python_api = fused_swiglu_impl
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x = np.random.uniform(-1, 1, self.x_shape).astype("float64")
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y = np.random.uniform(-1, 1, self.y_shape).astype("float64")
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out_grad = np.random.uniform(-1, 1, self.out_shape).astype("float64")
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res = swiglu(x, y, out_grad)
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self.inputs = {'x': x, 'y': y}
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self.outputs = {'out': res[0].numpy()}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(
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['x', 'y'],
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'out',
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)
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class TestSwigluOp_ZeroSize2(TestSwigluOp_ZeroSize):
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def config(self):
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self.x_shape = (1, 128)
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self.y_shape = (0, 128)
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self.out_shape = (0, 128)
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class TestSwigluOp_ZeroSize3(TestSwigluOp_ZeroSize):
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def config(self):
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self.x_shape = (0, 128)
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self.y_shape = (0, 128)
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self.out_shape = (0, 128)
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class TestSwigluGradOp(unittest.TestCase):
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def test_swiglu_grad(self):
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x = paddle.randn([10, 2]).astype("float32")
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out_grad = paddle.randn([10, 1]).astype("float32")
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x_grad, y_grad = paddle._C_ops.swiglu_grad(x, None, out_grad)
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self.assertFalse(
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paddle.all(x_grad == 0).item(), "x_grad should not be all zero"
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)
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# y_grad is not initialized when y is none
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if __name__ == "__main__":
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unittest.main()
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