[JAX] Local-Amax for Current-Scaling (#2183)

* Adding Amax Primitive and related args.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Enable local-amax for current-scaling and optionally run AR aross FSDP/TP/SP.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Adding doc for Amax Primitive.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Fix the function name conflict.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Modification as feedback suggested.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Fix errors from lint.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Fix the wrong amax-scope in the bwd.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Added more description for amax-scope
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Fix the wrong attribute name.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Keep dim for AmaxCalcuation.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Remove keepDim and add shardy_rule
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Fix shardy_rule
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Remove extra-collective bytes from ref_coll_count due to local amax.
Signed-off-by: Ming Huang <mingh@nvidia.com>

---------
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>
Co-authored-by: Phuong Nguyen <phuonguyen@nvidia.com>

tests/jax/test_distributed_layernorm.py

@@ -76,8 +76,6 @@ class TestDistributedLayernorm:
             all_reduce_loss_bytes + weight_count * shape[-1] * jax_dtype.itemsize
         )
         other_bytes = 0
-        if fp8_recipe == recipe.Float8CurrentScaling():
-            allreduce_total_bytes += jax_dtype.itemsize  # 1 * dtype for the amax reduction
         return generate_collectives_count(
             allreduce=allreduce_total_bytes * int(is_dp_enabled), allgather=0, other=other_bytes
         )

transformer_engine/jax/cpp_extensions/activation.py

@@ -26,7 +26,7 @@ from .misc import (
     should_apply_1x_fused_dbias_war_for_arch_l_100,
     NamedSharding,
 )
-from .quantization import _jax_dbias, _quantize_dbias_impl
+from .quantization import _jax_dbias, _quantize_dbias_impl, AmaxScope
 from ..sharding import all_reduce_max_along_all_axes_except_PP, all_reduce_sum_along_dp_fsdp
 from ..quantize import ScaledTensor, ScaledTensorFactory, NoScaleTensor
 from ..quantize import (
@@ -979,6 +979,7 @@ def act_lu(
     x: jnp.ndarray,
     activation_type: Sequence[Union[str, Callable]],
     quantizer: Optional[Quantizer] = None,
+    amax_scope: AmaxScope = AmaxScope.LOCAL,
 ) -> Union[jnp.ndarray, ScaledTensor]:
     """Activation with optional quantization.
 
@@ -987,6 +988,7 @@ def act_lu(
             Shape: (..., ACT_DIM, K) where ACT_DIM is 1 for non-gated activations and 2 for gated activations
         activation_type: Type of activation function to apply.
         quantizer: Optional quantizer for FP8 quantization of the output.
+        amax_scope: Indicate the scope to run amax calculation. This only works when using current-scaling. Default is AmaxScope.LOCAL.
 
     Returns:
         If quantizer is None:
@@ -1044,7 +1046,13 @@ def act_lu(
             activation_type=activation_type,
             quantizer=None,
         )
-        out, _ = _quantize_dbias_impl(out, is_dbias=False, quantizer=quantizer, dq_dtype=x.dtype)
+        out, _ = _quantize_dbias_impl(
+            out,
+            is_dbias=False,
+            quantizer=quantizer,
+            dq_dtype=x.dtype,
+            amax_scope=amax_scope,
+        )
         return out
 
     if isinstance(quantizer, DelayedScaleQuantizer):

transformer_engine/jax/cpp_extensions/base.py

@@ -173,7 +173,7 @@ class BasePrimitive(metaclass=ABCMeta):
 _primitive_registry = {}
 
 
-def register_primitive(cls):
+def register_primitive(cls, outer_only=False):
     """
     Register a JAX primitive and add it to the internal registry.
     """
@@ -186,6 +186,7 @@ def register_primitive(cls):
     def name_of_wrapper_p():
         return cls.name + "_wrapper"
 
+    if not outer_only:
         inner_p = core.Primitive(cls.name)
         dispatch.prim_requires_devices_during_lowering.add(inner_p)
         inner_p.multiple_results = cls.multiple_results

transformer_engine/jax/cpp_extensions/normalization.py

@@ -27,7 +27,7 @@ from .misc import (
     NamedSharding,
     get_cudnn_version,
 )
-from .quantization import _quantize_dbias_impl
+from .quantization import _quantize_dbias_impl, AmaxScope
 from ..sharding import all_reduce_max_along_all_axes_except_PP, all_reduce_sum_along_dp_fsdp
 from ..quantize import ScaledTensor, ScaledTensorFactory, NoScaleTensor
 from ..quantize import (
@@ -880,6 +880,7 @@ def layernorm_fwd(
     zero_centered_gamma: bool,
     epsilon: float,
     quantizer: Optional[Quantizer],
+    amax_scope: AmaxScope = AmaxScope.LOCAL,
 ) -> tuple[Union[jnp.ndarray, ScaledTensor], jnp.ndarray, jnp.ndarray]:
     """Layer normalization forward pass with optional quantization.
 
@@ -893,6 +894,7 @@ def layernorm_fwd(
         zero_centered_gamma: If True, gamma is zero-centered.
         epsilon: Small constant for numerical stability.
         quantizer: Optional quantizer for FP8 quantization of the output.
+        amax_scope: Indicate the scope to run amax calculation. This only works when using current-scaling. Default is AmaxScope.LOCAL.
 
     Returns:
         A tuple containing:
@@ -952,7 +954,13 @@ def layernorm_fwd(
             epsilon=epsilon,
             quantizer=None,
         )
-        out, _ = _quantize_dbias_impl(out, is_dbias=False, quantizer=quantizer, dq_dtype=x.dtype)
+        out, _ = _quantize_dbias_impl(
+            out,
+            is_dbias=False,
+            quantizer=quantizer,
+            dq_dtype=x.dtype,
+            amax_scope=amax_scope,
+        )
         return out, mu, rsigma
 
     is_2x2x = quantizer.is_2x2x()
@@ -1082,6 +1090,7 @@ def rmsnorm_fwd(
     zero_centered_gamma: bool,
     epsilon: float,
     quantizer: Optional[Quantizer],
+    amax_scope: AmaxScope = AmaxScope.LOCAL,
 ) -> tuple[Union[jnp.ndarray, ScaledTensor], jnp.ndarray]:
     """Root mean square normalization forward pass with optional quantization.
 
@@ -1093,6 +1102,7 @@ def rmsnorm_fwd(
         zero_centered_gamma: If True, gamma is zero-centered.
         epsilon: Small constant for numerical stability.
         quantizer: Optional quantizer for FP8 quantization of the output.
+        amax_scope: Indicate the scope to run amax calculation. This only works when using current-scaling. Default is AmaxScope.LOCAL.
 
     Returns:
         A tuple containing:
@@ -1153,7 +1163,11 @@ def rmsnorm_fwd(
             quantizer=None,
         )
         out, _ = _quantize_dbias_impl(
-            out.data, is_dbias=False, quantizer=quantizer, dq_dtype=x.dtype
+            out.data,
+            is_dbias=False,
+            quantizer=quantizer,
+            dq_dtype=x.dtype,
+            amax_scope=amax_scope,
         )
         return out, rsigma
 
@@ -1278,6 +1292,7 @@ def normalization_fwd(
     epsilon: float,
     norm_type: str,
     quantizer: Optional[Quantizer],
+    amax_scope: AmaxScope = AmaxScope.LOCAL,
 ):
     """Common wrapper for normalization forward pass.
 
@@ -1294,6 +1309,7 @@ def normalization_fwd(
             - 'layernorm': Layer normalization
             - 'rmsnorm': Root mean square normalization
         quantizer: Optional quantizer for FP8 quantization of the output.
+        amax_scope: Indicate the scope to run amax calculation. This only works when using current-scaling. Default is AmaxScope.LOCAL.
 
     Returns:
         A tuple containing:
@@ -1311,12 +1327,27 @@ def normalization_fwd(
         zero_centered_gamma is not supported if norm_type is 'rmsnorm'.
     """
     if norm_type == "layernorm":
-        output, mu, rsigma = layernorm_fwd(x, gamma, beta, zero_centered_gamma, epsilon, quantizer)
+        output, mu, rsigma = layernorm_fwd(
+            x,
+            gamma,
+            beta,
+            zero_centered_gamma,
+            epsilon,
+            quantizer,
+            amax_scope=amax_scope,
+        )
     elif norm_type == "rmsnorm":
         assert (
             not zero_centered_gamma
         ), "zero_centered_gamma is not supported if norm_type is 'rmsnorm'"
-        output, rsigma = rmsnorm_fwd(x, gamma, zero_centered_gamma, epsilon, quantizer)
+        output, rsigma = rmsnorm_fwd(
+            x,
+            gamma,
+            zero_centered_gamma,
+            epsilon,
+            quantizer,
+            amax_scope=amax_scope,
+        )
         mu = None
     else:
         raise ValueError(f"{norm_type=} is not supported.")

transformer_engine/jax/cpp_extensions/quantization.py

@@ -6,6 +6,8 @@ import operator
 from functools import reduce
 from typing import Tuple, Optional, Union
 import math
+from enum import Enum
+
 
 import jax
 import jax.numpy as jnp
@@ -26,7 +28,12 @@ from .misc import (
     get_min_device_compute_capability,
     NamedSharding,
 )
-from ..sharding import all_reduce_max_along_all_axes_except_PP, all_reduce_sum_along_dp_fsdp
+from ..sharding import (
+    all_reduce_max_along_all_axes_except_PP,
+    all_reduce_sum_along_dp_fsdp,
+    global_mesh_resource,
+    lax_paral_op,
+)
 from ..quantize import (
     ScaledTensor2x,
     ScaledTensor,
@@ -526,6 +533,126 @@ class QuantizePrimitive(BaseDBiasQuantizePrimitive):
     """Subclass of BaseDBiasQuantizePrimitive for quantization without dbias. No change in functionality from the base primitive but named differently for use in more granular disabling of primitives via NVTE_JAX_CUSTOM_CALLS."""
 
 
+class AmaxScope(Enum):
+    """
+    Amax Scope Enum
+    """
+
+    LOCAL = 1
+    TPSP = 2
+    FSDP = 3
+
+
+class AmaxCalculationPrimitive(BasePrimitive):
+    """
+    Amax Calculation Primitive with custom_partitioning
+    """
+
+    name = "jax_local_amax"
+    multiple_results = False
+    impl_static_args = (1,)  # amax_scope
+    inner_primitive = None
+    outer_primitive = None
+
+    @staticmethod
+    def abstract(
+        x_aval,
+        *,
+        amax_scope,
+    ):
+        """
+        amax calcuation abstract
+        """
+        del amax_scope
+
+        dtype = dtypes.canonicalize_dtype(x_aval.dtype)
+        assert dtype in [jnp.float32, jnp.float16, jnp.bfloat16]
+
+        out_aval = jax.core.ShapedArray(shape=(1,), dtype=jnp.float32)
+        return out_aval
+
+    @staticmethod
+    def impl(
+        x,
+        amax_scope,
+    ):
+        """
+        amax calcuation implementation
+        """
+        del amax_scope
+        amax = jnp.amax(jnp.abs(x), keepdims=True).astype(jnp.float32).reshape((1,))
+        return amax
+
+    @staticmethod
+    def infer_sharding_from_operands(
+        amax_scope,
+        mesh,
+        arg_infos,
+        result_infos,
+    ):
+        """
+        amax calcuation infer_sharding_from_operands
+        """
+        del (amax_scope, arg_infos, result_infos)  # Unused.
+        amax_sharding = NamedSharding(
+            mesh,
+            PartitionSpec(None),
+            desc="AmaxCalculationPrimitive.out_sharding",
+        )
+        return amax_sharding
+
+    @staticmethod
+    def partition(
+        amax_scope,
+        mesh,
+        arg_infos,
+        result_infos,
+    ):
+        """
+        amax calcuation partition
+        """
+        del result_infos
+
+        amax_sharding = NamedSharding(
+            mesh,
+            PartitionSpec(None),
+            desc="AmaxCalculationPrimitive.out_sharding",
+        )
+
+        def sharded_impl(x):
+            amax = AmaxCalculationPrimitive.impl(
+                x,
+                amax_scope=amax_scope,
+            )
+            if amax_scope is AmaxScope.TPSP:  # Run AR across TP/SP
+                gmesh = global_mesh_resource()
+                amax = lax_paral_op(amax, jax.lax.pmax, gmesh.tp_resource, mesh)
+                amax = lax_paral_op(amax, jax.lax.pmax, gmesh.tpsp_resource, mesh)
+
+            if amax_scope is AmaxScope.FSDP:  # Run AR across FSDP
+                gmesh = global_mesh_resource()
+                amax = lax_paral_op(amax, jax.lax.pmax, gmesh.fsdp_resource, mesh)
+
+            return amax
+
+        arg_shardings = tuple(arg_i.sharding for arg_i in arg_infos)
+        return mesh, sharded_impl, amax_sharding, arg_shardings
+
+    @staticmethod
+    def shardy_sharding_rule(amax_scope, mesh, value_types, result_types):
+        """
+        amax calcuation shardy_sharding_rule
+        """
+        del amax_scope, mesh, result_types
+        prefix = "AmaxCal"
+        input_spec = tuple(f"{prefix}_{i}" for i in range(len(value_types[0].shape)))
+        output_spec = (f"{prefix}_amax",)
+        return SdyShardingRule((input_spec,), (output_spec,))
+
+
+register_primitive(AmaxCalculationPrimitive, outer_only=True)
+
+
 def _jax_quantize(
     x, quantizer: Quantizer = None, dq_dtype: Optional[jnp.dtype] = None, flatten_axis: int = -1
 ):
@@ -572,6 +699,7 @@ def _quantize_dbias_impl(
     is_dbias: bool = False,
     dq_dtype: Optional[jnp.dtype] = None,
     flatten_axis: int = -1,
+    amax_scope: AmaxScope = AmaxScope.LOCAL,  # Only works when using current-scaling
 ) -> Tuple[ScaledTensor2x, jnp.ndarray]:
     """
     Cast wrapper
@@ -628,7 +756,10 @@ def _quantize_dbias_impl(
         # until the tensor is dequantized (e.g. in the GEMM).
         amax = x.amax
         if amax is None:
-            amax = jnp.amax(jnp.abs(x.data), keepdims=True).astype(jnp.float32).reshape((1,))
+            amax = AmaxCalculationPrimitive.outer_primitive.bind(
+                x.data,
+                amax_scope=amax_scope,
+            )
         scale = compute_scale_from_amax(amax, quantizer.q_dtype)
     elif quantizer.scaling_mode == ScalingMode.DELAYED_TENSOR_SCALING:
         scale = quantizer.scale
@@ -700,6 +831,7 @@ def quantize(
     x: Union[jnp.ndarray, NoScaleTensor],
     quantizer: Quantizer,
     flatten_axis: int = -1,
+    amax_scope: AmaxScope = AmaxScope.LOCAL,
 ) -> Tuple[ScaledTensor]:
     """Quantize input tensor according to the quantizer.
 
@@ -710,6 +842,7 @@ def quantize(
         flatten_axis: The quantization axis in which input data can be flattened to 2D for quantization.
             Defaults to -1.
             is None.
+        amax_scope: Indicate the scope to run amax calculation. This only works when using current-scaling. Default is AmaxScope.LOCAL.
 
     Returns:
         A ScaledTensor containing the quantized input tensor.
@@ -718,6 +851,7 @@ def quantize(
         x,
         quantizer=quantizer,
         flatten_axis=flatten_axis,
+        amax_scope=amax_scope,
     )
     return out
 
@@ -727,6 +861,7 @@ def quantize_dbias(
     quantizer: Quantizer,
     is_dbias: bool = True,
     flatten_axis: int = -1,
+    amax_scope: AmaxScope = AmaxScope.LOCAL,
 ) -> Tuple[ScaledTensor2x, jnp.ndarray]:
     """Quantize input tensor and compute bias gradient.
 
@@ -737,6 +872,8 @@ def quantize_dbias(
         is_dbias: If True, compute bias gradient. Defaults to True.
         flatten_axis: The quantization axis in which input data can be flattened to 2D for quantization.
             Defaults to -1.
+        amax_scope: Indicate the scope to run amax calculation. This only works when using current-scaling. Default is AmaxScope.LOCAL.
+
 
     Returns:
         A tuple containing:
@@ -750,6 +887,7 @@ def quantize_dbias(
         quantizer=quantizer,
         is_dbias=is_dbias,
         flatten_axis=flatten_axis,
+        amax_scope=amax_scope,
     )
 
 

transformer_engine/jax/dense.py

@@ -15,6 +15,7 @@ import jax
 import jax.numpy as jnp
 
 from . import cpp_extensions as tex
+from .cpp_extensions.quantization import AmaxScope
 from .quantize import (
     ScaledTensorFactory,
     ScalingMode,
@@ -64,6 +65,7 @@ def dense(
     input_axes: Tuple[str, ...] = None,
     kernel_axes: Tuple[str, ...] = None,
     quantizer_set: QuantizerSet = noop_quantizer_set,
+    using_global_amax_of_x: bool = False,
 ):
     """Perform dense layer transformation with optional quantization.
 
@@ -77,6 +79,7 @@ def dense(
         bias: Optional bias tensor to add after the transformation
         contracting_dims: Tuple of sequences specifying which dimensions to contract
         quantizer_set: QuantizerSet which contains quantizers for different tensor types
+        using_global_amax_of_x: Indicate wether to use global amax for x. Only works when using current-scaling. Default is False.
 
     Returns:
         Transformed output tensor
@@ -93,6 +96,7 @@ def dense(
         input_axes,
         kernel_axes,
         quantizer_set,
+        using_global_amax_of_x,
     )
     return output
 
@@ -103,6 +107,7 @@ def dense(
         3,
         4,
         5,
+        7,
     ),
 )
 def _dense(
@@ -113,6 +118,7 @@ def _dense(
     input_axes,
     kernel_axes,
     quantizer_set,
+    using_global_amax_of_x,
 ):
     """Internal implementation of dense layer transformation with custom VJP.
 
@@ -127,6 +133,7 @@ def _dense(
         input_axes: Logical axes for sharding the activation input
         kernel_axes: Logical axes for sharding the weight matrix
         quantizer_set: QuantizerSet which contains quantizers for different tensor types
+        using_global_amax_of_x: Indicate wether to use global amax for x. Only works when using current-scaling. Default is False.
 
     Returns:
         Transformed output tensor
@@ -139,6 +146,7 @@ def _dense(
         input_axes,
         kernel_axes,
         quantizer_set,
+        using_global_amax_of_x,
     )
     return output
 
@@ -151,6 +159,7 @@ def _dense_fwd_rule(
     input_axes,
     kernel_axes,
     quantizer_set,
+    using_global_amax_of_x,
 ):
     """Forward pass rule for dense layer transformation.
 
@@ -175,6 +184,7 @@ def _dense_fwd_rule(
         x,
         flatten_axis=flatten_axis_x,
         quantizer=quantizer_set.x,
+        amax_scope=AmaxScope.TPSP if using_global_amax_of_x else AmaxScope.LOCAL,
     )
     casted_x = with_sharding_constraint_by_logical_axes(casted_x, input_axes)
 
@@ -182,6 +192,7 @@ def _dense_fwd_rule(
         kernel,
         flatten_axis=flatten_axis_k,
         quantizer=quantizer_set.kernel,
+        amax_scope=AmaxScope.FSDP,
     )
     casted_kernel = with_sharding_constraint_by_logical_axes(casted_kernel, kernel_axes)
 
@@ -212,7 +223,7 @@ def _dense_fwd_rule(
 
 
 def _dense_bwd_rule(
-    contracting_dims, input_axes, kernel_axes, ctx, grad
+    contracting_dims, input_axes, kernel_axes, using_global_amax_of_x, ctx, grad
 ):  # pylint: disable=unused-argument
     """Backward pass rule for dense layer transformation.
 
@@ -238,6 +249,7 @@ def _dense_bwd_rule(
         is_dbias=use_bias,
         flatten_axis=flatten_axis_k,
         quantizer=quantizer_set.dgrad,
+        amax_scope=AmaxScope.LOCAL if using_global_amax_of_x else AmaxScope.TPSP,
     )
 
     # GEMM NT

transformer_engine/jax/layernorm_mlp.py

@@ -21,6 +21,7 @@ import jax.numpy as jnp
 from jax.ad_checkpoint import checkpoint_name
 
 from . import cpp_extensions as tex
+from .cpp_extensions.quantization import AmaxScope
 from .layernorm import canonicalize_norm_type
 from .quantize import (
     with_sharding_constraint_by_logical_axes,
@@ -272,13 +273,12 @@ def _layernorm_mlp_fwd_rule(
         epsilon,
         norm_type,
         quantizer=ffn1_quantizer_set.x,
+        amax_scope=AmaxScope.TPSP,
     )
     casted_ln_out = with_sharding_constraint_by_logical_axes(casted_ln_out, dot_1_input_axes)
 
     casted_kernel_1 = tex.quantize(
-        kernel_1,
-        flatten_axis=-2,
-        quantizer=ffn1_quantizer_set.kernel,
+        kernel_1, flatten_axis=-2, quantizer=ffn1_quantizer_set.kernel, amax_scope=AmaxScope.FSDP
     )
 
     # NN GEMM
@@ -317,6 +317,7 @@ def _layernorm_mlp_fwd_rule(
     casted_kernel_2 = tex.quantize(
         kernel_2,
         quantizer=ffn2_quantizer_set.kernel,
+        amax_scope=AmaxScope.FSDP,
     )
 
     # NN GEMM
@@ -417,6 +418,7 @@ def _layernorm_mlp_bwd_rule(
         grad,
         is_dbias=use_bias_2,
         quantizer=ffn1_quantizer_set.dgrad,
+        amax_scope=AmaxScope.TPSP,
     )
 
     # k_non_contracting_dims calibrated with the shape difference of grad.ndim vs kernel_1.ndim