[PYTHON][TENSORFLOW] Signature of function.forward() does not have to

match signature of kernel anymore

python/triton/function.py

@@ -62,11 +62,16 @@ class function(metaclass = function_meta):
     
     # Find a mapping between ::forward arguments and tensorflow op arguments
     op = result[0].op
-    remap = dict()
+    remap_in = dict()
     for i, ix in enumerate(op.inputs):
       for j, jx in enumerate(args):
         if ix is jx:
-          remap[j] = i
+          remap_in[j] = i
+    remap_out = []
+    for i, ix in enumerate(result):
+      for j, jx in enumerate(op.outputs):
+        if ix is jx:
+          remap_out.append(j)
 
     # Register backward pass
     ctx_registry[op] = ctx
@@ -75,14 +80,16 @@ class function(metaclass = function_meta):
       @fw.tensorflow.RegisterGradient(name)
       def gradient(op, *dy):
         dy = dy if len(dy) > 1 else dy[0]
-        grad = cls.backward(ctx_registry[op], dy)
+        # Remap gradient inputs in the right order
+        grads = [dy[i] for i in remap_out]
+        # Execute gradient function
+        grad = cls.backward(ctx_registry[op], grads)
         grad = function.extract_tf_tensors(grad, 'backward')
-
         # Remap gradient in the right order
         ret = [None] * len(op.inputs)
         for i in range(len(grad)):
-          if i in remap:
+          if i in remap_in:
-            ret[remap[i]] = grad[i]
+            ret[remap_in[i]] = grad[i]
         # Return
         return ret
       cls.registered = True

python/triton/kernel.py

@@ -225,8 +225,10 @@ class kernel:
     bench_id = libtriton.make_scalar_id() if bench > 0 else -1
     # call framework function
     if fw.has_tensorflow():
+      empty = [x for x in args[:-1] if isinstance(x, triton.utils.tf_empty_proxy)]
+      if len(empty) != len(self.outputs):
+        raise ValueError('Number of empty arguments does not much number of outputs provided')
       # operands
-      outputs = [x for x in args[:-1] if isinstance(x, triton.utils.tf_empty_proxy)]
       operands = [x.shape if isinstance(x, triton.utils.tf_empty_proxy) else x for x in args[:-1]]
       # output data types
       kwargs = {'id': op_id, 'bench': bench, 'bench_id': bench_id}
@@ -235,10 +237,12 @@ class kernel:
           kwargs['T' + str(i)] = x.dtype
       # launch
       ret = self.fw_op(*operands, **kwargs)
-      assert len(ret) == len(outputs)
+      # fill empty tensors with corresponding values
-      # record results
+      for j, y in enumerate(ret[0].op.op_def.output_arg):
-      for i in range(len(outputs)):
+        for i, x in enumerate(ret[0].op.op_def.input_arg):
-        outputs[i].tensor = ret[i]
+          if y.name + '_shape' == x.name:
+            empty[i].tensor = ret[j]
+      # store timing information
       if bench > 0:
         bench_registry[ret] = triton.utils.id_dict.lazy_entry(bench_id)
     elif fw.has_torch():

python/triton/ops/batchnorm.py

@@ -109,8 +109,7 @@ void bwdbatchnorm(float *DX, float *DG, float *DB,
     return y
 
   @staticmethod
-  def backward(ctx, grads):
+  def backward(ctx, dy):
-    dy, dmean, dvar = grads
     # retrieve info
     x, gamma, beta, mean, var = ctx.saved_tensors
     eps = ctx.eps