modified random sample test function

Author: xgqdut2016

test/infiniop/causal_softmax.py

@@ -21,7 +21,6 @@
 #  Configuration (Internal Use Only)
 # ==============================================================================
 # These are not meant to be imported from other modules
-
 _TEST_CASES = [
     # x_shape, x_stride
     ((32, 512), None),

test/infiniop/random_sample.py

@@ -22,7 +22,6 @@
 #  Configuration (Internal Use Only)
 # ==============================================================================
 # These are not meant to be imported from other modules
-
 _TEST_CASES = [
     # voc, random_val, topp, topk, temperature
     (512, 0.8, 0.8, 3, 0.5),
@@ -59,53 +58,52 @@ class RandomSampleDescriptor(Structure):
 
 
 def random_sample(data, random_val, topp, topk, voc, temperature, torch_device):
-    indices = torch.zeros([topk], dtype=torch.int64)
-    dataNp = data.clone().detach()
-    sorted_indices = torch.arange(voc)
-
-    for i in range(topk):
-        for j in range(i + 1, voc):
-            if dataNp[i] < dataNp[j]:
-                tmp = dataNp[i].clone().detach()
-                dataNp[i] = dataNp[j].clone().detach()
-                dataNp[j] = tmp
-
-                tmpInd = sorted_indices[i].clone().detach()
-                sorted_indices[i] = sorted_indices[j].clone().detach()
-                sorted_indices[j] = tmpInd
-
-    # sorted_indices = torch.argsort(dataNp, descending=True)
-    indices = sorted_indices[:topk]
-
-    dataNp = dataNp[sorted_indices]
-
-    globalM = dataNp[0]
-    dataNp = (dataNp - globalM) / temperature
-    dataNp = torch.softmax(dataNp.float(), dim=0)
-    sum_s = 0
-    for end in range(topk):
-        sum_s += dataNp[end]
-        if sum_s >= topp:
-            break
-    if end < topk - 1:
-        end += 1
+    if topp > 0 and topk > 1:
+        indices = torch.zeros([topk], dtype=torch.int64)
+        dataNp = data.clone().detach()
+        sorted_indices = torch.arange(voc)
+
+        for i in range(topk):
+            for j in range(i + 1, voc):
+                if dataNp[i] < dataNp[j]:
+                    tmp = dataNp[i].clone().detach()
+                    dataNp[i] = dataNp[j].clone().detach()
+                    dataNp[j] = tmp
+
+                    tmpInd = sorted_indices[i].clone().detach()
+                    sorted_indices[i] = sorted_indices[j].clone().detach()
+                    sorted_indices[j] = tmpInd
+
+        # sorted_indices = torch.argsort(dataNp, descending=True)
+        indices = sorted_indices[:topk]
+
+        dataNp = dataNp[sorted_indices]
+
+        globalM = dataNp[0]
+        dataNp = (dataNp - globalM) / temperature
+        dataNp = torch.softmax(dataNp.float(), dim=0)
+        sum_s = 0
+        for end in range(topk):
+            sum_s += dataNp[end]
+            if sum_s >= topp:
+                break
+        if end < topk - 1:
+            end += 1
+        else:
+            end = topk
+
+        sum_s = 0
+        for i in range(end):
+            sum_s += dataNp[i]
+        random_val *= sum_s
+
+        sum_s = 0
+        for i in range(end):
+            sum_s += dataNp[i]
+            if random_val < sum_s:
+                return indices[i]
     else:
-        end = topk
-
-    sum_s = 0
-    for i in range(end):
-        sum_s += dataNp[i]
-    random_val *= sum_s
-
-    sum_s = 0
-    for i in range(end):
-        sum_s += dataNp[i]
-        if random_val < sum_s:
-            return indices[i]
-
-
-def random_sample_0(data):
-    return torch.argmax(data)
+        return torch.argmax(data)
 
 
 def test(
@@ -124,12 +122,10 @@ def test(
     data = torch.arange(voc).float() * 0.0001
     _perm = torch.randperm(voc)
     data = data[_perm].to(x_dtype).to(torch_device)
-    if topp > 0 and topk > 1:
-        ans = random_sample(
-            data.to("cpu"), random_val, topp, topk, voc, temperature, "cpu"
-        )
-    else:
-        ans = random_sample_0(data)
+
+    ans = random_sample(
+        data, random_val, topp, topk, voc, temperature, torch_device
+    )  # 这个函数在device速度可能会很慢，可以通过data.to("cpu")方式加快计算过程
 
     indices = torch.zeros([1], dtype=torch.int64).to(torch_device)