[PyTorch] Remove dtype from args of permutation

docs/api/pytorch.rst

@@ -44,3 +44,7 @@ pyTorch
 .. autoapifunction:: transformer_engine.pytorch.make_graphed_callables
 
 .. autoapifunction:: transformer_engine.pytorch.get_cpu_offload_context
+
+.. autoapifunction:: transformer_engine.pytorch.moe_permute
+
+.. autoapifunction:: transformer_engine.pytorch.moe_unpermute

qa/L0_pytorch_unittest/test.sh

@@ -23,3 +23,4 @@ pytest -v -s $TE_PATH/tests/pytorch/test_fused_optimizer.py
 pytest -v -s $TE_PATH/tests/pytorch/test_multi_tensor.py
 pytest -v -s $TE_PATH/tests/pytorch/test_fusible_ops.py
 pytest -v -s $TE_PATH/tests/pytorch/test_fusible_ops_distributed.py
+pytest -v -s $TE_PATH/tests/pytorch/test_permutation.py

tests/pytorch/test_permutation.py

@@ -220,9 +220,7 @@ def _test_permutation(
     te_permute_fwd_input.requires_grad_(True)
     te_permute_bwd_input = permute_bwd_input if fp8 else pytorch_permute_bwd_input.detach()
 
-    te_permute_output, row_id_map = te_permute(
-        te_permute_fwd_input, te_dtype, indices, num_out_tokens
-    )
+    te_permute_output, row_id_map = te_permute(te_permute_fwd_input, indices, num_out_tokens)
     te_permute_output.backward(te_permute_bwd_input, retain_graph=True)
 
     te_probs = None
@@ -233,7 +231,7 @@ def _test_permutation(
     te_unpermute_fwd_input.requires_grad_(True)
     te_unpermute_bwd_input = unpermute_bwd_input if fp8 else pytorch_unpermute_bwd_input.detach()
 
-    te_unpermute_output = te_unpermute(te_unpermute_fwd_input, te_dtype, row_id_map, te_probs)
+    te_unpermute_output = te_unpermute(te_unpermute_fwd_input, row_id_map, te_probs)
     te_unpermute_output.backward(te_unpermute_bwd_input, retain_graph=True)
 
     ###################################################################################################################################
@@ -305,7 +303,7 @@ def backward_wrapper(
             lambda: pytorch_permute(pytorch_permute_fwd_input, indices, num_out_tokens)
         )
         t2 = perf_test_cuda_kernel(
-            lambda: te_permute(te_permute_fwd_input, te_dtype, indices, num_out_tokens)
+            lambda: te_permute(te_permute_fwd_input, indices, num_out_tokens)
         )
         print(f"permute\t\tfwd: pytorch: {t1:.3f} ms,  TE: {t2:.3f} ms")
 
@@ -333,7 +331,7 @@ def backward_wrapper(
             lambda: pytorch_unpermute(pytorch_unpermute_fwd_input, sorted_indices, probs=probs)
         )
         t2 = perf_test_cuda_kernel(
-            lambda: te_unpermute(te_unpermute_fwd_input, te_dtype, row_id_map, te_probs)
+            lambda: te_unpermute(te_unpermute_fwd_input, row_id_map, te_probs)
         )
         print(f"unpermute\tfwd: pytorch: {t1:.3f} ms,  TE: {t2:.3f} ms")
 

transformer_engine/pytorch/permutation.py

@@ -8,7 +8,8 @@
 import torch
 
 import transformer_engine_torch as tex
-from transformer_engine.pytorch.float8_tensor import Float8Tensor
+from .constants import TE_DType
+from .float8_tensor import Float8Tensor
 
 
 __all__ = [
@@ -27,14 +28,13 @@ class _moe_permute(torch.autograd.Function):
     def forward(
         ctx,
         inp: torch.Tensor,
-        dtype: tex.DType,
         indices: torch.Tensor,
         num_out_tokens: int,
         max_token_num: int,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         # Empty input check
         if not inp.numel():
-            return inp, None
+            return inp, torch.tensor([], device=inp.device)
 
         # Device check
         assert inp.is_cuda, "TransformerEngine needs CUDA."
@@ -43,16 +43,13 @@ def forward(
         assert inp.size(0) == indices.size(0), "Permute not possible"
 
         # Data type check
-        fp8 = False
-        if dtype in [tex.DType.kFloat8E4M3, tex.DType.kFloat8E5M2]:
-            fp8 = True
+        fp8 = isinstance(inp, Float8Tensor)
         if fp8:
-            assert isinstance(
-                inp, Float8Tensor
-            ), "Input must be in Float8Tensor type for FP8 moe_permute."
-            fp8_dtype = inp._fp8_dtype
+            dtype = inp._fp8_dtype
             fp8_scale_inv = inp._scale_inv
             inp = inp._data
+        else:
+            dtype = TE_DType[inp.dtype]
         if indices.dtype != torch.int32:
             warnings.warn(
                 f"The data type of the input `indices` of Permute is {indices.dtype}! "
@@ -78,13 +75,12 @@ def forward(
 
         if fp8:
             permuted_act = Float8Tensor(
-                data=permuted_act, fp8_dtype=fp8_dtype, fp8_scale_inv=fp8_scale_inv
+                data=permuted_act, fp8_dtype=dtype, fp8_scale_inv=fp8_scale_inv
             )
 
         ctx.row_id_map = row_id_map
         ctx.num_tokens = indices.size(0)
         ctx.topK = indices.size(1)
-        ctx.dtype = dtype
         ctx.fp8 = fp8
         return permuted_act, row_id_map
 
@@ -101,30 +97,27 @@ def backward(
         if not permuted_act_grad.is_contiguous():
             permuted_act_grad = permuted_act_grad.contiguous()
 
-        fp8 = ctx.fp8
-        if fp8:
+        if ctx.fp8:
             assert isinstance(
                 permuted_act_grad, Float8Tensor
             ), "Grad of the output must be in Float8Tensor type for FP8 moe_permute."
-            fp8_dtype = permuted_act_grad._fp8_dtype
+            dtype = permuted_act_grad._fp8_dtype
             fp8_scale_inv = permuted_act_grad._scale_inv
             permuted_act_grad = permuted_act_grad._data
-
-        row_id_map = ctx.row_id_map
-        num_tokens = ctx.num_tokens
-        topK = ctx.topK
+        else:
+            dtype = TE_DType[permuted_act_grad.dtype]
 
         act_grad = None
         if ctx.needs_input_grad[0]:
             act_grad = tex.moe_permute_bwd(
-                permuted_act_grad, ctx.dtype, row_id_map, torch.empty(0), num_tokens, topK
+                permuted_act_grad, dtype, ctx.row_id_map, torch.empty(0), ctx.num_tokens, ctx.topK
             )
-            if fp8:
+            if ctx.fp8:
                 act_grad = Float8Tensor(
-                    data=act_grad, fp8_dtype=fp8_dtype, fp8_scale_inv=fp8_scale_inv * topK
+                    data=act_grad, fp8_dtype=dtype, fp8_scale_inv=fp8_scale_inv * ctx.topK
                 )
 
-        return act_grad, None, None, None, None
+        return act_grad, None, None, None
 
 
 class _moe_unpermute(torch.autograd.Function):
@@ -134,7 +127,6 @@ class _moe_unpermute(torch.autograd.Function):
     def forward(
         ctx,
         inp: torch.Tensor,
-        dtype: tex.DType,
         row_id_map: torch.Tensor,
         probs: torch.Tensor,
     ) -> torch.Tensor:
@@ -166,16 +158,13 @@ def forward(
         assert row_id_map.is_cuda, "TransformerEngine needs CUDA."
 
         # Data type check
-        fp8 = False
-        if dtype in [tex.DType.kFloat8E4M3, tex.DType.kFloat8E5M2]:
-            fp8 = True
+        fp8 = isinstance(inp, Float8Tensor)
         if fp8:
-            assert isinstance(
-                inp, Float8Tensor
-            ), "Input must be in Float8Tensor type for FP8 moe_unpermute."
-            fp8_dtype = inp._fp8_dtype
+            dtype = inp._fp8_dtype
             fp8_scale_inv = inp._scale_inv
             inp = inp._data
+        else:
+            dtype = TE_DType[inp.dtype]
         if row_id_map.dtype != torch.int32:
             warnings.warn(
                 f"The data type of the input `row_id_map` of Unpermute is {row_id_map.dtype}! "
@@ -187,10 +176,9 @@ def forward(
 
         if fp8:
             unpermuted_output = Float8Tensor(
-                data=unpermuted_output, fp8_dtype=fp8_dtype, fp8_scale_inv=fp8_scale_inv
+                data=unpermuted_output, fp8_dtype=dtype, fp8_scale_inv=fp8_scale_inv
             )
 
-        ctx.dtype = dtype
         ctx.save_for_backward(inp, row_id_map, probs)
         ctx.fp8 = fp8
         return unpermuted_output
@@ -207,35 +195,33 @@ def backward(
         if not unpermuted_act_grad.is_contiguous():
             unpermuted_act_grad = unpermuted_act_grad.contiguous()
 
-        fp8 = ctx.fp8
-        if fp8:
+        if ctx.fp8:
             assert isinstance(
                 unpermuted_act_grad, Float8Tensor
             ), "Grad of the output must be in Float8Tensor type for FP8 moe_unpermute."
-            fp8_dtype = unpermuted_act_grad._fp8_dtype
+            dtype = unpermuted_act_grad._fp8_dtype
             fp8_scale_inv = unpermuted_act_grad._scale_inv
             unpermuted_act_grad = unpermuted_act_grad._data
+        else:
+            dtype = TE_DType[unpermuted_act_grad.dtype]
 
         inp, row_id_map, probs = ctx.saved_tensors
 
         act_grad = None
         if ctx.needs_input_grad[0]:
             act_grad, prob_grad = tex.moe_unpermute_bwd(
-                unpermuted_act_grad, inp, ctx.dtype, row_id_map, probs
+                unpermuted_act_grad, inp, dtype, row_id_map, probs
             )
-            if fp8:
-                act_grad = Float8Tensor(
-                    data=act_grad, fp8_dtype=fp8_dtype, fp8_scale_inv=fp8_scale_inv
-                )
-        if not ctx.needs_input_grad[3]:
+            if ctx.fp8:
+                act_grad = Float8Tensor(data=act_grad, fp8_dtype=dtype, fp8_scale_inv=fp8_scale_inv)
+        if not ctx.needs_input_grad[2]:
             prob_grad = None
 
-        return act_grad, None, None, prob_grad
+        return act_grad, None, prob_grad
 
 
 def moe_permute(
     inp: torch.Tensor,
-    dtype: tex.DType,
     indices: torch.Tensor,
     num_out_tokens: int = -1,
     max_token_num: int = -1,
@@ -247,8 +233,6 @@ def moe_permute(
     ----------
     inp: torch.Tensor
         Input tensor of shape `[num_tokens, hidden_size]`, on which permutation will be applied.
-    dtype: tex.DType
-        Data type of the input tensor.
     indices: torch.Tensor
         The token to expert indices tensor of shape [num_tokens, topK] and dtype 'int32'.
     num_out_tokens: int, default = -1
@@ -259,12 +243,11 @@ def moe_permute(
         By default, set to '-1', meaning the calculation of the size of workspace is
         automatically taken over by the operator.
     """
-    return _moe_permute.apply(inp, dtype, indices, num_out_tokens, max_token_num)
+    return _moe_permute.apply(inp, indices, num_out_tokens, max_token_num)
 
 
 def moe_unpermute(
     inp: torch.Tensor,
-    dtype: tex.DType,
     row_id_map: torch.Tensor,
     probs: torch.Tensor = None,
 ) -> torch.Tensor:
@@ -276,8 +259,6 @@ def moe_unpermute(
     ----------
     inp: torch.Tensor
         Input tensor with permuted tokens of shape `[num_tokens, hidden_size]` to be unpermuted.
-    dtype: tex.DType
-        Data type of the input tensor.
     row_id_map: torch.Tensor
         The tensor of a mapping table for sorted indices used to unpermute the tokens,
         which is the second output tensor of `Permute`.
@@ -286,4 +267,4 @@ def moe_unpermute(
         the unpermuted tokens will be merged with their respective probabilities.
         By default, set to an empty tensor, which means that the tokens are directly merged by accumulation.
     """
-    return _moe_unpermute.apply(inp, dtype, row_id_map, probs)
+    return _moe_unpermute.apply(inp, row_id_map, probs)