[JAX] Scale swizzling via JAX transpose op (#2163)

* add swizzle in jax

Signed-off-by: Phuong Nguyen <[email protected]>

* added outer_impl

Signed-off-by: Phuong Nguyen <[email protected]>

* clean up FFI

Signed-off-by: Phuong Nguyen <[email protected]>

---------

Signed-off-by: Phuong Nguyen <[email protected]>

Author: phu0ngng

transformer_engine/jax/cpp_extensions/base.py

@@ -134,6 +134,13 @@ def impl():
         """
         return NotImplemented
 
+    @classmethod
+    def outer_impl(cls, *args, **kwargs):
+        """
+        to describe implementation for outer primitive
+        """
+        return cls.impl(*args, **kwargs)
+
     @staticmethod
     @abstractmethod
     def batcher():
@@ -196,7 +203,7 @@ def name_of_wrapper_p():
     outer_p = core.Primitive(name_of_wrapper_p())
     dispatch.prim_requires_devices_during_lowering.add(outer_p)
     outer_p.multiple_results = cls.multiple_results
-    outer_p.def_impl(cls.impl)
+    outer_p.def_impl(cls.outer_impl)
     outer_p.def_abstract_eval(cls.outer_abstract)
     batching.primitive_batchers[outer_p] = cls.batcher
     outer_p_lower = custom_partitioning(cls.impl, static_argnums=cls.impl_static_args)

transformer_engine/jax/cpp_extensions/gemm.py

@@ -152,6 +152,21 @@ def _quantize_gemm_operands(lhs, rhs, lhs_quantizer, rhs_quantizer, contracting_
     return lhs_q, rhs_q
 
 
+@partial(jax.jit, static_argnums=(1, 2))
+def swizzled_scale(scale_inv, flatten_axis, is_colwise):
+    "Swizzle scale_inv via JAX transpose ops"
+    original_shape = scale_inv.shape
+    shape_2d = (math.prod(original_shape[:flatten_axis]), math.prod(original_shape[flatten_axis:]))
+    if is_colwise:
+        scale_inv = jnp.transpose(scale_inv.reshape(shape_2d))
+        cols, rows = shape_2d
+    else:
+        rows, cols = shape_2d
+    reshape = scale_inv.reshape(rows // 128, 4, 32, cols // 4, 4)
+    swizzled = jnp.transpose(reshape, (0, 3, 2, 1, 4))
+    return swizzled.reshape(original_shape)
+
+
 class GemmPrimitive(BasePrimitive):
     """
     Primitive for cuBLAS GEMM
@@ -286,28 +301,18 @@ def _dims_are_consecutive(dims):
                 )
         pre_gelu_out = jax.core.ShapedArray(shape=pre_gelu_shape, dtype=pre_gelu_dtype)
 
-        # Need extra workspace for swizzled scale factors
-        lhs_swizzle_size = 0
-        rhs_swizzle_size = 0
-        swizzle_dtype = jnp.uint8
-        if scaling_mode == ScalingMode.MXFP8_1D_SCALING:
-            lhs_swizzle_size = lhs_scale_inv.size
-            rhs_swizzle_size = rhs_scale_inv.size
-        lhs_swizzle = jax.core.ShapedArray(shape=(lhs_swizzle_size,), dtype=swizzle_dtype)
-        rhs_swizzle = jax.core.ShapedArray(shape=(rhs_swizzle_size,), dtype=swizzle_dtype)
-
         # Declare cuBLAS workspace
         # cuBLAS workspace ptr must be 256 bytes aligned but JAX buffers are not
         # necessarily 256 bytes aligned, we add some padding to ensure alignment.
         workspace_size = get_cublas_workspace_size_bytes() + 256
         workspace = jax.core.ShapedArray(shape=(workspace_size,), dtype=jnp.uint8)
 
-        return output, bias_grad, pre_gelu_out, lhs_swizzle, rhs_swizzle, workspace
+        return output, bias_grad, pre_gelu_out, workspace
 
     @staticmethod
     def outer_abstract(*args, **kwargs):
         outputs = GemmPrimitive.abstract(*args, **kwargs)
-        return outputs[:-3]  # discard workspace arrays
+        return outputs[:-1]  # discard workspace array
 
     @staticmethod
     def lowering(
@@ -374,24 +379,22 @@ def impl(
         grad,
         use_split_accumulator,
     ):
-        lhs_cdims, rhs_cdims = map(sanitize_dims, (lhs.ndim, rhs.ndim), contracting_dims)
-        lhs_transposed, rhs_transposed = _get_gemm_layout(
-            (lhs.ndim, rhs.ndim), (lhs_cdims, rhs_cdims)
-        )
-        lhs_scale_inv = apply_padding_to_scale_inv(
-            lhs_scale_inv,
-            scaling_mode,
-            lhs.shape,
-            is_colwise=lhs_transposed,
-            flatten_axis=max(lhs_cdims) + 1 if lhs_transposed else min(lhs_cdims),
-        )
-        rhs_scale_inv = apply_padding_to_scale_inv(
-            rhs_scale_inv,
-            scaling_mode,
-            rhs.shape,
-            is_colwise=not rhs_transposed,
-            flatten_axis=min(rhs_cdims) if rhs_transposed else max(rhs_cdims) + 1,
-        )
+        if scaling_mode.is_1d_block_scaling():
+            lhs_cdims, rhs_cdims = map(sanitize_dims, (lhs.ndim, rhs.ndim), contracting_dims)
+            lhs_transposed, rhs_transposed = _get_gemm_layout(
+                (lhs.ndim, rhs.ndim), (lhs_cdims, rhs_cdims)
+            )
+            lhs_flatten_axis = max(lhs_cdims) + 1 if lhs_transposed else min(lhs_cdims)
+            rhs_flatten_axis = min(rhs_cdims) if rhs_transposed else max(rhs_cdims) + 1
+
+            lhs_scale_inv = apply_padding_to_scale_inv(
+                lhs_scale_inv, scaling_mode, lhs.shape, lhs_transposed, lhs_flatten_axis
+            )
+            rhs_scale_inv = apply_padding_to_scale_inv(
+                rhs_scale_inv, scaling_mode, rhs.shape, not rhs_transposed, rhs_flatten_axis
+            )
+            lhs_scale_inv = swizzled_scale(lhs_scale_inv, lhs_flatten_axis, lhs_transposed)
+            rhs_scale_inv = swizzled_scale(rhs_scale_inv, rhs_flatten_axis, not rhs_transposed)
 
         outputs = GemmPrimitive.inner_primitive.bind(
             lhs,
@@ -408,7 +411,39 @@ def impl(
             grad=grad,
             use_split_accumulator=use_split_accumulator,
         )
-        return outputs[:-3]  # discard workspace arrays
+        return outputs[:-1]  # discard workspace array
+
+    @staticmethod
+    def outer_impl(
+        lhs,
+        lhs_scale_inv,
+        rhs,
+        rhs_scale_inv,
+        bias,
+        gelu_input,
+        out_dtype,
+        contracting_dims,
+        scaling_mode,
+        fuse_bias,
+        fuse_gelu,
+        grad,
+        use_split_accumulator,
+    ):
+        return GemmPrimitive.impl(
+            lhs,
+            lhs_scale_inv,
+            rhs,
+            rhs_scale_inv,
+            bias,
+            gelu_input,
+            out_dtype,
+            contracting_dims,
+            scaling_mode,
+            fuse_bias,
+            fuse_gelu,
+            grad,
+            use_split_accumulator,
+        )
 
     @staticmethod
     def batcher(

transformer_engine/jax/csrc/extensions/gemm.cpp

@@ -28,8 +28,8 @@ static uint8_t *move_ptr_to_next_256B_aligned(uint8_t *ptr) {
 }
 
 std::tuple<TensorWrapper, std::vector<size_t>> xla_buffer_to_nvte_gemm_operand(
-    cudaStream_t stream, Buffer_Type buffer, Buffer_Type scale_inv, Result_Type swizzled_scale_inv,
-    JAXX_Scaling_Mode scaling_mode, size_t axis_boundary, bool rowwise) {
+    cudaStream_t stream, Buffer_Type buffer, Buffer_Type scale_inv, JAXX_Scaling_Mode scaling_mode,
+    size_t axis_boundary, bool rowwise) {
   // Set tensor data with collapsed 2D shape
   auto buffer_dims = buffer.dimensions();
   std::vector<size_t> input_shape = {product(buffer_dims, 0, axis_boundary),
@@ -61,40 +61,6 @@ std::tuple<TensorWrapper, std::vector<size_t>> xla_buffer_to_nvte_gemm_operand(
     } else {
       input.set_columnwise_scale_inv(scale_inv.untyped_data(), scale_dtype, scale_shape);
     }
-
-    // Swizzle scaling factors for MXFP8
-    if (scaling_mode == JAXX_Scaling_Mode::MXFP8_1D_SCALING) {
-      // Get the swizzle buffer
-      NVTE_CHECK(swizzled_scale_inv->element_count() > 0,
-                 "Missing swizzled inverse scale buffer in the JAX primitive.");
-      auto scale_inv_dtype = convert_ffi_datatype_to_te_dtype(scale_inv.element_type());
-      auto swizzled_scale_inv_dtype =
-          convert_ffi_datatype_to_te_dtype(swizzled_scale_inv->element_type());
-      NVTE_CHECK(typeToSize(scale_inv_dtype) == 1 && typeToSize(swizzled_scale_inv_dtype) == 1,
-                 "Inverse scale factors need to have an 8-bit data type.");
-
-      // Create tensor to hold swizzled scale factor
-      TensorWrapper output(get_nvte_scaling_mode(scaling_mode));
-      if (rowwise) {
-        output.set_rowwise_data(buffer.untyped_data(), input_dtype, input_shape);
-        output.set_rowwise_scale_inv(swizzled_scale_inv->untyped_data(), scale_dtype, scale_shape);
-      } else {
-        output.set_columnwise_data(buffer.untyped_data(), input_dtype, input_shape);
-        output.set_columnwise_scale_inv(swizzled_scale_inv->untyped_data(), scale_dtype,
-                                        scale_shape);
-      }
-
-      // Launch swizzle kernel
-      nvte_swizzle_scaling_factors(input.data(), output.data(), stream);
-
-      // Set swizzled scales into the input tensor
-      if (rowwise) {
-        input.set_rowwise_scale_inv(swizzled_scale_inv->untyped_data(), scale_dtype, scale_shape);
-      } else {
-        input.set_columnwise_scale_inv(swizzled_scale_inv->untyped_data(), scale_dtype,
-                                       scale_shape);
-      }
-    }
   }
 
   return std::make_tuple(std::move(input), input_shape);
@@ -103,21 +69,19 @@ std::tuple<TensorWrapper, std::vector<size_t>> xla_buffer_to_nvte_gemm_operand(
 Error_Type GemmFFI(cudaStream_t stream, Buffer_Type lhs, Buffer_Type lhs_scale_inv, Buffer_Type rhs,
                    Buffer_Type rhs_scale_inv, Buffer_Type bias, Buffer_Type gelu_input,
                    Result_Type output, Result_Type bias_grad, Result_Type pre_gelu_out,
-                   Result_Type lhs_swizzle, Result_Type rhs_swizzle, Result_Type workspace,
-                   JAXX_Scaling_Mode scaling_mode, int64_t lhs_axis_boundary,
+                   Result_Type workspace, JAXX_Scaling_Mode scaling_mode, int64_t lhs_axis_boundary,
                    int64_t rhs_axis_boundary, bool lhs_transposed, bool rhs_transposed,
                    bool fuse_bias, bool fuse_gelu, bool grad, bool use_split_accumulator) {
-  // Operands (this includes swizzling MXFP8 scaling factors)
   // NOTE: TensorWrapper operands are always rowwise for full-precision GEMM, or FP8 GEMM when
   //       device supports non-TN layouts (compute capability >= 10.0, excluding 12.x)
   bool always_rowwise = (scaling_mode == JAXX_Scaling_Mode::NO_SCALING ||
                          (is_tensor_scaling(scaling_mode) && nvte_is_non_tn_fp8_gemm_supported()));
   bool make_lhs_rowwise = (always_rowwise) ? true : !lhs_transposed;
   bool make_rhs_rowwise = (always_rowwise) ? true : rhs_transposed;
-  auto [lhs_, lhs_shape] = xla_buffer_to_nvte_gemm_operand(
-      stream, lhs, lhs_scale_inv, lhs_swizzle, scaling_mode, lhs_axis_boundary, make_lhs_rowwise);
-  auto [rhs_, rhs_shape] = xla_buffer_to_nvte_gemm_operand(
-      stream, rhs, rhs_scale_inv, rhs_swizzle, scaling_mode, rhs_axis_boundary, make_rhs_rowwise);
+  auto [lhs_, lhs_shape] = xla_buffer_to_nvte_gemm_operand(stream, lhs, lhs_scale_inv, scaling_mode,
+                                                           lhs_axis_boundary, make_lhs_rowwise);
+  auto [rhs_, rhs_shape] = xla_buffer_to_nvte_gemm_operand(stream, rhs, rhs_scale_inv, scaling_mode,
+                                                           rhs_axis_boundary, make_rhs_rowwise);
 
   // Output tensor
   std::vector<size_t> out_shape = {(lhs_transposed) ? lhs_shape[1] : lhs_shape[0],
@@ -188,8 +152,6 @@ XLA_FFI_DEFINE_HANDLER_SYMBOL(GemmHandler, GemmFFI,
                                   .Ret<Buffer_Type>()      // output
                                   .Ret<Buffer_Type>()      // bias_grad
                                   .Ret<Buffer_Type>()      // pre_gelu_out
-                                  .Ret<Buffer_Type>()      // lhs_swizzled
-                                  .Ret<Buffer_Type>()      // rhs_swizzled
                                   .Ret<Buffer_Type>()      // workspace
                                   .Attr<JAXX_Scaling_Mode>("scaling_mode")
                                   .Attr<int64_t>("lhs_axis_boundary")