add jittor mpops (#227)

gammagl/mpops/__init__.py

@@ -1,3 +1,4 @@
+
 # !/usr/bin/env python3
 # -*- coding:utf-8 -*-
 
@@ -19,6 +20,9 @@
 
     elif os.environ['TL_BACKEND'] == 'torch':
         from .torch import *
+
+    elif os.environ['TL_BACKEND'] == 'jittor':
+        from .jittor import *
 
     else:
         raise NotImplementedError("This backend is not supported")

gammagl/mpops/jittor.py

@@ -0,0 +1,123 @@
+import jittor as jt
+
+
+def unsorted_segment_sum(x, segment_ids, num_segments):
+    if num_segments is None:
+        num_segments = int(segment_ids.asnumpy().max() + 1)
+
+    segment_ids = jt.array(segment_ids, dtype=jt.int64)
+    assert x.shape[0] == segment_ids.shape[0], "the length of segment_ids should be equal to data.shape[0]."
+    if len(segment_ids.shape) == 1:
+        s = jt.prod(jt.array(tuple(x.shape[1:]))).to(jt.int32).item()
+        segment_ids = segment_ids.repeat_interleave(s).view(segment_ids.shape[0], *x.shape[1:])
+
+    assert x.shape == segment_ids.shape, "data.shape and segment_ids.shape should be equal"
+
+    shape = [num_segments] + list(x.shape[1:])
+    tensor = jt.zeros(*shape).to(x.dtype).scatter(0, segment_ids, x, 'add')
+    return tensor
+
+def unsorted_segment_mean(x, segment_ids, num_segments=None):
+    if num_segments is None:
+        num_segments = int(segment_ids.numpy().max() + 1)
+
+    segment_ids = jt.array(segment_ids, dtype=jt.int64)
+    assert x.shape[0] == segment_ids.shape[0], "the length of segment_ids should be equal to data.shape[0]."
+    res = []
+    for i in range(num_segments):
+        mask_index = segment_ids == i
+        if jt.any(mask_index):
+            a = jt.mean(x[mask_index], 0)
+            res.append(a)
+        else:
+            a = jt.zeros_like(x[0])
+            res.append(a)
+    if res[0].shape == [1]:
+        return jt.concat(res, 0)
+    else:
+        return jt.stack(res, 0)
+
+def unsorted_segment_max(x, segment_ids, num_segments=None):
+    if num_segments is None:
+        num_segments = int(segment_ids.numpy().max() + 1)
+
+    segment_ids = jt.array(segment_ids, dtype=jt.int64)
+    assert x.shape[0] == segment_ids.shape[0], "the length of segment_ids should be equal to data.shape[0]."
+    res = []
+    for i in range(num_segments):
+        mask_index = segment_ids == i
+        if jt.any(mask_index):
+            res.append(jt.max(x[mask_index], 0)[0])
+        else:
+            a = jt.zeros_like(x[0])
+            a.fill_(jt.array(float('-inf')).to(a.dtype))
+            res.append(a)
+    if res[0].shape == [1]:
+        return jt.concat(res, 0)
+    else:
+        return jt.stack(res, 0)
+
+
+def segment_sum(x, segment_ids, num_segments=None):
+    if num_segments is None:
+        num_segments = int(segment_ids.numpy().max() + 1)
+
+    segment_ids = jt.array(segment_ids, dtype=jt.int64)
+    assert x.shape[0] == segment_ids.shape[0], "the length of segment_ids should be equal to data.shape[0]."
+    if len(segment_ids.shape) == 1:
+        s = jt.prod(jt.array(x.shape[1:])).to(jt.int32)
+        segment_ids = segment_ids.repeat_interleave(s).view(segment_ids.shape[0], *x.shape[1:])
+
+    assert x.shape == segment_ids.shape, "data.shape and segment_ids.shape should be equal"
+
+    shape = [num_segments] + list(x.shape[1:])
+    tensor = jt.zeros(*shape).to(x.dtype).scatter_add(0, segment_ids, x)
+    return tensor
+
+
+
+def segment_mean(x, segment_ids, num_segments=None):
+    if num_segments is None:
+        num_segments = int(segment_ids.numpy().max() + 1)
+
+    segment_ids = jt.array(segment_ids, dtype=jt.int64)
+    assert x.shape[0] == segment_ids.shape[0], "the length of segment_ids should be equal to data.shape[0]."
+    res = []
+    for i in range(num_segments):
+        mask_index = segment_ids == i
+        if jt.any(mask_index):
+            a = jt.mean(x[mask_index], 0)
+            res.append(a)
+        else:
+            a = jt.zeros_like(x[0])
+            res.append(a)
+    if res[0].shape == [1]:
+        return jt.concat(res, 0)
+    else:
+        return jt.stack(res, 0)
+
+def segment_max(x, segment_ids, num_segments=None):
+    if num_segments is None:
+        num_segments = int(segment_ids.numpy().max() + 1)
+
+    segment_ids = jt.array(segment_ids, dtype=jt.int64)
+    assert x.shape[0] == segment_ids.shape[0], "the length of segment_ids should be equal to data.shape[0]."
+    res = []
+    for i in range(num_segments):
+        mask_index = segment_ids == i
+        if jt.any(mask_index):
+            res.append(jt.max(x[mask_index], 0)[0])
+        else:
+            a = jt.zeros_like(x[0])
+            a.fill_(jt.array(float('-inf')).to(a.dtype))
+            res.append(a)
+    if res[0].shape == [1]:
+        return jt.concat(res, 0)
+    else:
+        return jt.stack(res, 0)
+
+def gspmm(index, weight=None, x=None, reduce='sum'):
+    pass
+
+def bspmm(index, weight=None, x=None, reduce='sum'):
+    pass