Merge pull request AI-Hypercomputer#4198 from AI-Hypercomputer:fix/fp8_qwix

PiperOrigin-RevId: 934430447

Author: Google-ML-Automation

src/maxtext/layers/nnx_decoders.py

@@ -25,7 +25,7 @@
 import jax.numpy as jnp
 from flax import linen as nn
 from flax import nnx
-from flax.nnx import wrappers as nnx_wrappers
+from maxtext.layers import nnx_wrappers
 from jax.ad_checkpoint import checkpoint_name
 from jax.sharding import Mesh
 
@@ -939,17 +939,8 @@ def pure_layer_fn(state_in, y_in):
       out = merged_layer(y_in, **kwargs)
       return out, nnx.state(merged_layer)
 
-    # Linen FP8 ops keep amax_history in mutable Linen scope; jax.checkpoint
-    # re-traces and hits UnexpectedTracerError. Skip remat for FP8.
-    uses_linen_fp8_mutable_state = self.config.quantization in {
-        "fp8_nanoo",
-        "fp8_gpu",
-    }
-    if uses_linen_fp8_mutable_state:
-      out, new_state = pure_layer_fn(state, y)
-    else:
-      checkpointed_fn = jax.checkpoint(pure_layer_fn, policy=policy, prevent_cse=prevent_cse)
-      out, new_state = checkpointed_fn(state, y)
+    checkpointed_fn = jax.checkpoint(pure_layer_fn, policy=policy, prevent_cse=prevent_cse)
+    out, new_state = checkpointed_fn(state, y)
     nnx.update(layer, new_state)
 
     return out
@@ -1077,26 +1068,7 @@ def layer_fn(carry, scanned_vars):
       params = nnx_ensure_scan_leading_axis(params, length)
       state = nnx_ensure_scan_leading_axis(state, length)
 
-      # Linen FP8 ops keep amax_history in mutable Linen scope; jax.lax.scan
-      # leaks the tracer and hits UnexpectedTracerError. Use a Python for-loop
-      # for FP8 instead.
-      uses_linen_fp8_mutable_state = self.config.quantization in {
-          "fp8_nanoo",
-          "fp8_gpu",
-      }
-      if uses_linen_fp8_mutable_state:
-        carry = x_in
-        per_layer_states = []
-        for i in range(length):
-          current_params = jax.tree.map(lambda x, i=i: x[i], params)
-          current_state = jax.tree.map(lambda x, i=i: x[i], state)
-          carry, new_state_i = layer_fn(carry, (current_params, current_state))
-          per_layer_states.append(new_state_i)
-        final_carry = carry
-        # pylint: disable-next=no-value-for-parameter  (*per_layer_states supplies the `tree` arg)
-        scanned_state = jax.tree.map(lambda *xs: jnp.stack(list(xs)), *per_layer_states)
-      else:
-        final_carry, scanned_state = jax.lax.scan(layer_fn_wrapped, x_in, (params, state))
+      final_carry, scanned_state = jax.lax.scan(layer_fn_wrapped, x_in, (params, state))
       returned_kv_stacked = None
 
     if scan_axis != 0:

src/maxtext/layers/nnx_wrappers.py

@@ -285,14 +285,18 @@ def __call__(
       # Get `mutable` from top level bridge.Module context if any
       if mutable is not None:
         pass
-      elif (m := bdg_module.current_module()) is not None:
+      elif getattr(bdg_module.MODULE_CONTEXT, "module_stack", None) and (m := bdg_module.current_module()) is not None:
         assert m.scope is not None
         mutable = m.scope.mutable
       elif (m := current_linen_module()) is not None:
         assert m.scope is not None
         mutable = m.scope.mutable
       else:
-        mutable = False
+        # Safe fallback mutability: when running functionally isolated inside standard JAX transforms,
+        # we determine which collections (such as "stats" or "amax_history") are present and mark them mutable.
+        mutable = [k for k in variables.keys() if k != "params"]
+        if not mutable:
+          mutable = False
 
       out = self.to_nnx__module.apply(variables, *args, rngs=_rngs, method=method, mutable=mutable, **kwargs)
 
@@ -509,7 +513,31 @@ def maybe_unbox(x):
       for path, _ in unknown_state_flat.items():
         paths_str += f"\n  - {'/'.join(map(str, path))}"
 
-      warnings.warn(f"Found unknown module paths in incoming state:{paths_str}")
+        # Dynamically reconstruct the unknown variables
+        curr = module
+        for p in path[:-1]:
+          if isinstance(curr, dict):
+            if p not in curr:
+              curr[p] = nnx.Module()
+            curr = curr[p]
+          elif isinstance(curr, list):
+            if not isinstance(p, int):
+              raise TypeError(f"Expected int index for list, got {type(p)}: {p}")
+            while len(curr) <= p:
+              curr.append(nnx.Module())
+            curr = curr[p]
+          elif isinstance(curr, tuple):
+            raise ValueError(f"Cannot dynamically reconstruct elements within a tuple at path {path}.")
+          else:
+            if not isinstance(p, str):
+              p = str(p)
+            if not hasattr(curr, p):
+              setattr(curr, p, nnx.Module())
+            curr = getattr(curr, p)
+
+      warnings.warn(
+          f"Found unknown module paths in incoming state:{paths_str}. Intermediate modules have been reconstructed."
+      )
 
     _fix_for_qwix_quantization(module)
     nnx.update(module, new_state)

src/maxtext/layers/quantizations.py

@@ -38,6 +38,14 @@
 from flax.linen import fp8_ops
 from flax.linen import initializers as flax_initializers
 import flax.linen as nn
+from flax import nnx
+# Support different packaging structures across environments even within
+# the same Qwix version identifier (imports from _src.utils vs _src).
+try:
+  from qwix._src.utils import flax_util
+except ImportError:
+  from qwix._src import flax_util  # pytype: disable=import-error
+from maxtext.layers import nnx_wrappers
 
 from maxtext.common.common_types import DType, Config
 from maxtext.inference.kvcache import KVQuant
@@ -710,6 +718,32 @@ def configure_kv_quant(config):
   return None if not config.quantize_kvcache else KVQuant(config)
 
 
+def _apply_linen_module_in_nnx(linen_module_cls, op_id, *args, **kwargs):
+  """Applies a Linen module within an NNX context."""
+  try:
+    parent = flax_util.get_current_module()
+    is_nnx = isinstance(parent, nnx.Module)
+  except ValueError:
+    is_nnx = False
+
+  if is_nnx:
+    attr_name = f"_qwix_fp8_gpu_{op_id}"
+    if not hasattr(parent, attr_name):
+      rngs = getattr(parent, "qwix_rngs", None)
+      if rngs is None:
+        parent_rngs = getattr(parent, "rngs", None)
+        if parent_rngs is not None and hasattr(parent_rngs, "fork"):
+          rngs = parent_rngs.fork()
+        else:
+          rngs = nnx.Rngs(0)
+      wrapper = nnx_wrappers.ToNNX(linen_module_cls(name=op_id), rngs=rngs)
+      wrapper.lazy_init(*args, **kwargs)
+      setattr(parent, attr_name, wrapper)
+    return getattr(parent, attr_name)(*args, mutable=["_overwrite_with_gradient"], **kwargs)
+  else:
+    return linen_module_cls(name=op_id)(*args, **kwargs)
+
+
 class NvidaFp8Provider(qwix.QtProvider):
   """Wraps nn.Fp8DirectDotGeneralOp with Qwix's provider interface."""
 
@@ -718,13 +752,13 @@ def dot_general(self, *args, **kwargs):
     rule, op_id = self._get_current_rule_and_op_id("dot_general")
     if rule is None:
       return jax.lax.dot_general(*args, **kwargs)
-    return nn.Fp8DirectDotGeneralOp(name=op_id)(*args, **kwargs)
+    return _apply_linen_module_in_nnx(nn.Fp8DirectDotGeneralOp, op_id, *args, **kwargs)
 
   def einsum(self, *args, **kwargs):
     rule, op_id = self._get_current_rule_and_op_id("einsum")
     if rule is None:
       return jnp.einsum(*args, **kwargs)
-    return nn.Fp8Einsum(name=op_id)(*args, **kwargs)
+    return _apply_linen_module_in_nnx(nn.Fp8Einsum, op_id, *args, **kwargs)
 
 
 class NANOOFp8Provider(qwix.QtProvider):
@@ -734,7 +768,7 @@ def dot_general(self, *args, **kwargs):
     rule, op_id = self._get_current_rule_and_op_id("dot_general")
     if rule is None:
       return jax.lax.dot_general(*args, **kwargs)
-    return nn.NANOOFp8DotGeneralOp(name=op_id)(*args, **kwargs)
+    return _apply_linen_module_in_nnx(nn.NANOOFp8DotGeneralOp, op_id, *args, **kwargs)
 
 
 def get_fp8_full_qwix_rule_w_sparsity(config: Config):
@@ -815,7 +849,21 @@ def maybe_quantize_model(model, config):
   if config.use_qwix_quantization and not config.use_batch_split_schedule:
     quantization_provider = get_qt_provider(config)
     if quantization_provider:
-      model = qwix.quantize_model(model, quantization_provider)
+      if config.pure_nnx:
+        input_shape = (config.micro_batch_size_to_train_on, config.max_target_length)
+        dummy_tokens = jnp.ones(input_shape, dtype=jnp.int32)
+        dummy_positions = jnp.ones(input_shape, dtype=jnp.int32)
+        dummy_segment_ids = jnp.ones(input_shape, dtype=jnp.int32)
+        model = qwix.quantize_model(
+            model,
+            quantization_provider,
+            dummy_tokens,
+            dummy_positions,
+            dummy_segment_ids,
+            enable_dropout=False,
+        )
+      else:
+        model = qwix.quantize_model(model, quantization_provider)
   return model
 
 

src/maxtext/trainers/pre_train/train.py

@@ -38,6 +38,7 @@
 
 from flax import linen as nn, nnx
 from flax.linen import partitioning as nn_partitioning
+from flax.nnx import variablelib
 
 from maxtext.configs import pyconfig
 from maxtext.utils.globals import EPS
@@ -359,7 +360,9 @@ def train_step(model, config, state_mesh_shardings, params_shardings, state, dat
           is_train=True,
       )
     else:
-      model_graphdef, curr_params, rest = nnx.split(state.model, nnx.Param, ...)
+      owg_type = variablelib.variable_type_from_name("_overwrite_with_gradient", allow_register=True)
+      custom_param_filter = nnx.Any(owg_type)
+      model_graphdef, curr_params, custom_params, rest = nnx.split(state.model, nnx.Param, custom_param_filter, ...)
       if config.parameter_memory_host_offload:
         # Params are kept on host (pinned_host) in in_shardings. Move only Param
         # variables to device before the forward/backward pass so that all dot_general
@@ -381,15 +384,15 @@ def train_step(model, config, state_mesh_shardings, params_shardings, state, dat
         )
         nnx.update(state.model, curr_params)
 
-      def diff_wrapper(param, rest, config, data):
-        local_model = nnx.merge(model_graphdef, param, rest, copy=True)
+      def diff_wrapper(curr_params, custom_params, rest, config, data):
+        local_model = nnx.merge(model_graphdef, curr_params, custom_params, rest, copy=True)
         loss, aux = loss_fn(local_model, config, data, None, None, is_train=True)
-        _, _, new_rest = nnx.split(local_model, nnx.Param, ...)
+        _, _, _, new_rest = nnx.split(local_model, nnx.Param, custom_param_filter, ...)
         return loss, (aux, new_rest)
 
-      grad_func = jax.value_and_grad(diff_wrapper, argnums=0, has_aux=True)
-      (loss, (aux, new_rest)), raw_grads = grad_func(curr_params, rest, config, data)
-      nnx.update(state.model, new_rest)
+      grad_func = jax.value_and_grad(diff_wrapper, argnums=(0, 1), has_aux=True)
+      (loss, (aux, new_rest)), (raw_grads, custom_grads) = grad_func(curr_params, custom_params, rest, config, data)
+      nnx.update(state.model, nnx.State.merge(custom_grads, new_rest))
 
   raw_grads = jax.tree_util.tree_map(
       lambda x: x.astype(config.grad_dtype) if x.dtype == jnp.float32 else x,