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Commit c1ac176e authored by rusty1s's avatar rusty1s
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parent 5909accb
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Showing with 125 additions and 139 deletions
cpu/scatter.cpp
View file @ c1ac176e
...@@ -4,19 +4,15 @@ ...@@ -4,19 +4,15 @@
void scatter_mul(at::Tensor src, at::Tensor index, at::Tensor out, void scatter_mul(at::Tensor src, at::Tensor index, at::Tensor out,
int64_t dim) { int64_t dim) {
printf("HUHUHUHU"); int64_t elems_per_row = index.size(dim), i, idx;
// int64_t elems_per_row = index.size(dim), i, idx; AT_DISPATCH_ALL_TYPES(src.type(), "scatter_mul", [&] {
// printf("elems_per_row: %lli\n", elems_per_row); DIM_APPLY3(scalar_t, src, int64_t, index, scalar_t, out, dim, {
// AT_DISPATCH_ALL_TYPES(src.type(), "scatter_mul", [&] { for (i = 0; i < elems_per_row; i++) {
// DIM_APPLY3(scalar_t, src, int64_t, index, scalar_t, out, dim, { idx = index_data[i * index_stride];
// for (i = 0; i < elems_per_row; i++) { out_data[idx * out_stride] *= src_data[i * src_stride];
// idx = index_data[i * index_stride]; }
// printf("i: %lli, idx: %lli\n", i, idx); });
// printf("src: %lli\n", (int64_t)src_data[i * src_stride]); });
// out_data[idx * out_stride] *= src_data[i * src_stride];
// }
// });
// });
} }
void scatter_div(at::Tensor src, at::Tensor index, at::Tensor out, void scatter_div(at::Tensor src, at::Tensor index, at::Tensor out,
......
test/test_backward.py
View file @ c1ac176e
...@@ -2,7 +2,7 @@ from itertools import product ...@@ -2,7 +2,7 @@ from itertools import product
import pytest import pytest
import torch import torch
# from torch.autograd import gradcheck from torch.autograd import gradcheck
import torch_scatter import torch_scatter
from .utils import grad_dtypes as dtypes, devices, tensor from .utils import grad_dtypes as dtypes, devices, tensor
...@@ -13,14 +13,13 @@ indices = [2, 0, 1, 1, 0] ...@@ -13,14 +13,13 @@ indices = [2, 0, 1, 1, 0]
@pytest.mark.parametrize('func,device', product(funcs, devices)) @pytest.mark.parametrize('func,device', product(funcs, devices))
def test_backward(func, device): def test_backward(func, device):
pass index = torch.tensor(indices, dtype=torch.long, device=device)
# index = torch.tensor(indices, dtype=torch.long, device=device) src = torch.rand((index.size(0), 2), dtype=torch.double, device=device)
# src = torch.rand((index.size(0), 2), dtype=torch.double, device=device) src.requires_grad_()
# src.requires_grad_()
# op = getattr(torch_scatter, 'scatter_{}'.format(func)) op = getattr(torch_scatter, 'scatter_{}'.format(func))
# data = (src, index, 0) data = (src, index, 0)
# assert gradcheck(op, data, eps=1e-6, atol=1e-4) is True assert gradcheck(op, data, eps=1e-6, atol=1e-4) is True
tests = [{ tests = [{
...@@ -44,13 +43,12 @@ tests = [{ ...@@ -44,13 +43,12 @@ tests = [{
@pytest.mark.parametrize('test,dtype,device', product(tests, dtypes, devices)) @pytest.mark.parametrize('test,dtype,device', product(tests, dtypes, devices))
def test_arg_backward(test, dtype, device): def test_arg_backward(test, dtype, device):
pass src = tensor(test['src'], dtype, device)
# src = tensor(test['src'], dtype, device) src.requires_grad_()
# src.requires_grad_() index = tensor(test['index'], torch.long, device)
# index = tensor(test['index'], torch.long, device) grad = tensor(test['grad'], dtype, device)
# grad = tensor(test['grad'], dtype, device)
op = getattr(torch_scatter, 'scatter_{}'.format(test['name']))
# op = getattr(torch_scatter, 'scatter_{}'.format(test['name'])) out, _ = op(src, index, test['dim'], fill_value=test['fill_value'])
# out, _ = op(src, index, test['dim'], fill_value=test['fill_value']) out.backward(grad)
# out.backward(grad) assert src.grad.tolist() == test['expected']
# assert src.grad.tolist() == test['expected']
test/test_forward.py
View file @ c1ac176e
...@@ -6,110 +6,108 @@ import torch_scatter ...@@ -6,110 +6,108 @@ import torch_scatter
from .utils import dtypes, devices, tensor from .utils import dtypes, devices, tensor
dtypes = [torch.float]
tests = [{ tests = [{
# 'name': 'add', 'name': 'add',
# 'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]], 'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
# 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]], 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
# 'dim': -1, 'dim': -1,
# 'fill_value': 0, 'fill_value': 0,
# 'expected': [[0, 0, 4, 3, 3, 0], [2, 4, 4, 0, 0, 0]], 'expected': [[0, 0, 4, 3, 3, 0], [2, 4, 4, 0, 0, 0]],
# }, { }, {
# 'name': 'add', 'name': 'add',
# 'src': [[5, 2], [2, 5], [4, 3], [1, 3]], 'src': [[5, 2], [2, 5], [4, 3], [1, 3]],
# 'index': [0, 1, 1, 0], 'index': [0, 1, 1, 0],
# 'dim': 0, 'dim': 0,
# 'fill_value': 0, 'fill_value': 0,
# 'expected': [[6, 5], [6, 8]], 'expected': [[6, 5], [6, 8]],
# }, { }, {
# 'name': 'sub', 'name': 'sub',
# 'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]], 'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
# 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]], 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
# 'dim': -1, 'dim': -1,
# 'fill_value': 9, 'fill_value': 9,
# 'expected': [[9, 9, 5, 6, 6, 9], [7, 5, 5, 9, 9, 9]], 'expected': [[9, 9, 5, 6, 6, 9], [7, 5, 5, 9, 9, 9]],
# }, { }, {
# 'name': 'sub', 'name': 'sub',
# 'src': [[5, 2], [2, 2], [4, 2], [1, 3]], 'src': [[5, 2], [2, 2], [4, 2], [1, 3]],
# 'index': [0, 1, 1, 0], 'index': [0, 1, 1, 0],
# 'dim': 0, 'dim': 0,
# 'fill_value': 9, 'fill_value': 9,
# 'expected': [[3, 4], [3, 5]], 'expected': [[3, 4], [3, 5]],
# }, { }, {
'name': 'mul', 'name': 'mul',
'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]], 'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]], 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
'dim': -1, 'dim': -1,
'fill_value': 1, 'fill_value': 1,
'expected': [[1, 1, 4, 3, 2, 0], [0, 4, 3, 1, 1, 1]], 'expected': [[1, 1, 4, 3, 2, 0], [0, 4, 3, 1, 1, 1]],
# }, { }, {
# 'name': 'mul', 'name': 'mul',
# 'src': [[5, 2], [2, 5], [4, 3], [1, 3]], 'src': [[5, 2], [2, 5], [4, 3], [1, 3]],
# 'index': [0, 1, 1, 0], 'index': [0, 1, 1, 0],
# 'dim': 0, 'dim': 0,
# 'fill_value': 1, 'fill_value': 1,
# 'expected': [[5, 6], [8, 15]], 'expected': [[5, 6], [8, 15]],
# }, { }, {
# 'name': 'div', 'name': 'div',
# 'src': [[2, 1, 1, 4, 2], [1, 2, 1, 2, 4]], 'src': [[2, 1, 1, 4, 2], [1, 2, 1, 2, 4]],
# 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]], 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
# 'dim': -1, 'dim': -1,
# 'fill_value': 1, 'fill_value': 1,
# 'expected': [[1, 1, 0.25, 0.5, 0.5, 1], [0.5, 0.25, 0.5, 1, 1, 1]], 'expected': [[1, 1, 0.25, 0.5, 0.5, 1], [0.5, 0.25, 0.5, 1, 1, 1]],
# }, { }, {
# 'name': 'div', 'name': 'div',
# 'src': [[4, 2], [2, 1], [4, 2], [1, 2]], 'src': [[4, 2], [2, 1], [4, 2], [1, 2]],
# 'index': [0, 1, 1, 0], 'index': [0, 1, 1, 0],
# 'dim': 0, 'dim': 0,
# 'fill_value': 1, 'fill_value': 1,
# 'expected': [[0.25, 0.25], [0.125, 0.5]], 'expected': [[0.25, 0.25], [0.125, 0.5]],
# }, { }, {
# 'name': 'mean', 'name': 'mean',
# 'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]], 'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
# 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]], 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
# 'dim': -1, 'dim': -1,
# 'fill_value': 0, 'fill_value': 0,
# 'expected': [[0, 0, 4, 3, 1.5, 0], [1, 4, 2, 0, 0, 0]], 'expected': [[0, 0, 4, 3, 1.5, 0], [1, 4, 2, 0, 0, 0]],
# }, { }, {
# 'name': 'mean', 'name': 'mean',
# 'src': [[5, 2], [2, 5], [4, 3], [1, 3]], 'src': [[5, 2], [2, 5], [4, 3], [1, 3]],
# 'index': [0, 1, 1, 0], 'index': [0, 1, 1, 0],
# 'dim': 0, 'dim': 0,
# 'fill_value': 0, 'fill_value': 0,
# 'expected': [[3, 2.5], [3, 4]], 'expected': [[3, 2.5], [3, 4]],
# }, { }, {
# 'name': 'max', 'name': 'max',
# 'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]], 'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
# 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]], 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
# 'dim': -1, 'dim': -1,
# 'fill_value': 0, 'fill_value': 0,
# 'expected': [[0, 0, 4, 3, 2, 0], [2, 4, 3, 0, 0, 0]], 'expected': [[0, 0, 4, 3, 2, 0], [2, 4, 3, 0, 0, 0]],
# 'expected_arg': [[-1, -1, 3, 4, 0, 1], [1, 4, 3, -1, -1, -1]], 'expected_arg': [[-1, -1, 3, 4, 0, 1], [1, 4, 3, -1, -1, -1]],
# }, { }, {
# 'name': 'max', 'name': 'max',
# 'src': [[5, 2], [2, 5], [4, 3], [1, 3]], 'src': [[5, 2], [2, 5], [4, 3], [1, 3]],
# 'index': [0, 1, 1, 0], 'index': [0, 1, 1, 0],
# 'dim': 0, 'dim': 0,
# 'fill_value': 0, 'fill_value': 0,
# 'expected': [[5, 3], [4, 5]], 'expected': [[5, 3], [4, 5]],
# 'expected_arg': [[0, 3], [2, 1]], 'expected_arg': [[0, 3], [2, 1]],
# }, { }, {
# 'name': 'min', 'name': 'min',
# 'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]], 'src': [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]],
# 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]], 'index': [[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]],
# 'dim': -1, 'dim': -1,
# 'fill_value': 9, 'fill_value': 9,
# 'expected': [[9, 9, 4, 3, 1, 0], [0, 4, 1, 9, 9, 9]], 'expected': [[9, 9, 4, 3, 1, 0], [0, 4, 1, 9, 9, 9]],
# 'expected_arg': [[-1, -1, 3, 4, 2, 1], [0, 4, 2, -1, -1, -1]], 'expected_arg': [[-1, -1, 3, 4, 2, 1], [0, 4, 2, -1, -1, -1]],
# }, { }, {
# 'name': 'min', 'name': 'min',
# 'src': [[5, 2], [2, 5], [4, 3], [1, 3]], 'src': [[5, 2], [2, 5], [4, 3], [1, 3]],
# 'index': [0, 1, 1, 0], 'index': [0, 1, 1, 0],
# 'dim': 0, 'dim': 0,
# 'fill_value': 9, 'fill_value': 9,
# 'expected': [[1, 2], [2, 3]], 'expected': [[1, 2], [2, 3]],
# 'expected_arg': [[3, 0], [1, 2]], 'expected_arg': [[3, 0], [1, 2]],
}] }]
...@@ -118,15 +116,12 @@ def test_forward(test, dtype, device): ...@@ -118,15 +116,12 @@ def test_forward(test, dtype, device):
src = tensor(test['src'], dtype, device) src = tensor(test['src'], dtype, device)
index = tensor(test['index'], torch.long, device) index = tensor(test['index'], torch.long, device)
expected = tensor(test['expected'], dtype, device) expected = tensor(test['expected'], dtype, device)
print(src)
print(index)
op = getattr(torch_scatter, 'scatter_{}'.format(test['name'])) op = getattr(torch_scatter, 'scatter_{}'.format(test['name']))
out = op(src, index, test['dim'], fill_value=test['fill_value']) out = op(src, index, test['dim'], fill_value=test['fill_value'])
print(out)
# if isinstance(out, tuple): if isinstance(out, tuple):
# assert out[0].tolist() == expected.tolist() assert out[0].tolist() == expected.tolist()
# assert out[1].tolist() == test['expected_arg'] assert out[1].tolist() == test['expected_arg']
# else: else:
# assert out.tolist() == expected.tolist() assert out.tolist() == expected.tolist()
torch_scatter/mul.py
View file @ c1ac176e
...@@ -7,11 +7,8 @@ from torch_scatter.utils.gen import gen ...@@ -7,11 +7,8 @@ from torch_scatter.utils.gen import gen
class ScatterMul(Function): class ScatterMul(Function):
@staticmethod @staticmethod
def forward(ctx, out, src, index, dim): def forward(ctx, out, src, index, dim):
print("DRIN")
func = get_func('scatter_mul', src) func = get_func('scatter_mul', src)
print(func)
func(src, index, out, dim) func(src, index, out, dim)
print(out)
ctx.mark_dirty(out) ctx.mark_dirty(out)
ctx.save_for_backward(out, src, index) ctx.save_for_backward(out, src, index)
......
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