Add checkpoint_name

Signed-off-by: Reese Wang <[email protected]>

transformer_engine/jax/flax/module.py

@@ -16,6 +16,7 @@
 from jax import lax
 from jax import nn as jax_nn
 from jax import random as jax_random
+from jax.ad_checkpoint import checkpoint_name
 
 from ..dot import type_safe_dot_general
 from ..fp8 import FP8Helper, FP8MetaPackage
@@ -923,6 +924,8 @@ def kernel_1_init(key, num_kernels, stack_axis, *init_args):
                 bias_shape = (1,) * (x.ndim - bias.ndim) + bias.shape
                 x += jnp.reshape(bias, bias_shape)
 
+            x = checkpoint_name(x, 'ffn1')
+
             activations = []
             if is_geglu(self.activations):
                 z = geglu(x)
@@ -957,4 +960,6 @@ def kernel_1_init(key, num_kernels, stack_axis, *init_args):
                 bias = bias.astype(self.dtype)
                 out += jnp.reshape(bias, (1,) * (out.ndim - 1) + (-1,))
 
+            out = checkpoint_name(out, 'ffn2')
+
         return out, ln_output    # Output, layner_norm_output

transformer_engine/jax/flax/transformer.py

@@ -20,6 +20,7 @@
 from jax import nn as jax_nn
 from jax import random as jax_random
 from jax import lax, vmap
+from jax.ad_checkpoint import checkpoint_name
 
 from .module import DenseGeneral, LayerNormDenseGeneral, LayerNormMLP
 from .module import LayerNorm, Softmax
@@ -211,6 +212,8 @@ def core_attention(query: Array,
     else:
         attn_weights = jnp.einsum('bqhd,bkhd->bhqk', query, key)
 
+    attn_weights = checkpoint_name(attn_weights, 'logits')
+
     attn_weights = _with_sharding_constraint(attn_weights,
                                              (BATCH_AXES, HEAD_AXES, SEQLEN_AXES, SEQLEN_AXES))
 
@@ -499,6 +502,7 @@ def _check_head_dim(head_dim):
                     bias_axes=(W_JOINED_AXES, W_TP_AXES),
                     name='qkv',
                     dtype=self.dtype)(inputs_q)
+                qkv_proj = checkpoint_name(qkv_proj, 'combined_qkv_proj')
                 if not use_fused_attn:
                     query, key, value = jnp.split(qkv_proj, [1, 2], axis=-2)
             else:
@@ -530,6 +534,7 @@ def _check_head_dim(head_dim):
                                        bias_axes=(W_JOINED_AXES, W_TP_AXES),
                                        name='kv',
                                        dtype=self.dtype)(inputs_kv)
+                kv_proj = checkpoint_name(kv_proj, 'combined_kv_proj')
                 if not use_fused_attn:
                     key, value = jnp.split(kv_proj, [1], axis=-2)
         else:
@@ -574,6 +579,9 @@ def _check_head_dim(head_dim):
             residual = ln_out
 
         if not use_fused_attn:
+            query = checkpoint_name(query, 'query_proj')
+            key = checkpoint_name(key, 'key_proj')
+            value = checkpoint_name(value, 'value_proj')
             query = query.reshape((query.shape[0], query.shape[1], self.num_heads, self.head_dim))
             key = key.reshape((key.shape[0], key.shape[1], self.num_heads, self.head_dim))
             value = value.reshape((value.shape[0], value.shape[1], self.num_heads, self.head_dim))
@@ -706,6 +714,8 @@ def convert_to_softmax_type(attn_mask_type, mask):
                                dtype=self.dtype,
                                float32_logits=self.float32_logits)
 
+            x = checkpoint_name(x, 'context')
+
         x = x.reshape((x.shape[0], x.shape[1], x.shape[2] * x.shape[3]))
 
         attn_context_sharding_constraint = \
@@ -724,6 +734,7 @@ def convert_to_softmax_type(attn_mask_type, mask):
                            bias_axes=(W_NO_SHARD_AXES,),
                            dtype=self.dtype,
                            name='out')(x)
+        out = checkpoint_name(out, 'out_proj')
         return out, residual
 
 

transformer_engine/jax/fused_attn.py

@@ -5,6 +5,7 @@
 
 from enum import Enum
 from functools import partial
+from jax.ad_checkpoint import checkpoint_name
 import jax
 import jax.numpy as jnp
 
@@ -91,6 +92,9 @@ def _self_fused_attn_fwd_rule(qkv: jnp.ndarray, bias: jnp.ndarray, mask: jnp.nda
                                                          scaling_factor=scaling_factor,
                                                          dropout_probability=dropout_probability,
                                                          is_training=is_training)
+    output = checkpoint_name(output, 'context')
+    softmax_aux = checkpoint_name(softmax_aux, 'context')
+    rng_state = checkpoint_name(rng_state, 'context')
     return output, (qkv, bias, softmax_aux, rng_state, output, squeezed_mask)