[JAX] Flax module init with a given dtype (#1472)

* flax module to init params with given dtype

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

* all tests passed

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

* remove unneccessary reshape for kernel

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

* remove casting output of dot

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

* clean up

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

---------

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

transformer_engine/jax/cpp_extensions/attention.py

@@ -252,8 +252,13 @@ def abstract(
         k_dtype = dtypes.canonicalize_dtype(k_aval.dtype)
         v_dtype = dtypes.canonicalize_dtype(v_aval.dtype)
         bias_dtype = dtypes.canonicalize_dtype(bias_aval.dtype)
-        assert q_dtype == k_dtype == v_dtype == bias_dtype
-        assert q_seqlen_or_cu_seqlen_aval.dtype == kv_seqlen_or_cu_seqlen_aval.dtype
+        assert (
+            q_dtype == k_dtype == v_dtype == bias_dtype
+        ), f"q_dtype={q_dtype}, k_dtype={k_dtype}, v_dtype={v_dtype}, bias_dtype={bias_dtype}"
+        assert q_seqlen_or_cu_seqlen_aval.dtype == kv_seqlen_or_cu_seqlen_aval.dtype, (
+            f"q_seqlen_or_cu_seqlen_aval={q_seqlen_or_cu_seqlen_aval},"
+            f" kv_seqlen_or_cu_seqlen_aval={kv_seqlen_or_cu_seqlen_aval}"
+        )
 
         batch_shape, q_max_seqlen, kv_max_seqlen, attn_heads, num_gqa_groups, head_dim = (
             FusedAttnHelper.parse_qkv_aval(q_aval, k_aval, v_aval, config.qkv_layout)

transformer_engine/jax/dot.py

@@ -25,7 +25,7 @@ def type_safe_dot_general(
     """
 
     if fp8_meta_pkg is None:
-        kernel = jnp.asarray(kernel, x.dtype)
+        assert x.dtype == kernel.dtype, f"lhs dtype = {x.dtype}, rhs dtype = {kernel.dtype}"
         return jax.lax.dot_general(x, kernel, (contracting_dims, ((), ())))
 
     amax_list = fp8_meta_pkg.amax_list

transformer_engine/jax/flax/module.py

@@ -59,17 +59,13 @@ def _obtain_default_layernorm_scale_init_if_need(original_init, zero_centered_ga
 def _create_layernorm_parameters(
     layernorm_type, shape, scale_init, scale_axes, bias_init, bias_axes, dtype
 ):
-    scale = nn_partitioning.param_with_axes(
-        "scale", scale_init, shape, jnp.float32, axes=scale_axes
-    )
-    scale = jnp.asarray(scale, dtype)
+    scale = nn_partitioning.param_with_axes("scale", scale_init, shape, dtype, axes=scale_axes)
+    scale = scale.astype(dtype)
 
     layernorm_type = canonicalize_layernorm_type(layernorm_type)
     if layernorm_type == "layernorm":
-        bias = nn_partitioning.param_with_axes(
-            "ln_bias", bias_init, shape, jnp.float32, axes=bias_axes
-        )
-        bias = jnp.asarray(bias, dtype)
+        bias = nn_partitioning.param_with_axes("ln_bias", bias_init, shape, dtype, axes=bias_axes)
+        bias = bias.astype(dtype)
     else:
         assert layernorm_type == "rmsnorm"
         bias = None
@@ -280,7 +276,8 @@ class LayerNorm(nn.Module):  # pylint: disable=too-few-public-methods
 
     def __post_init__(self):
         self.scale_init = _obtain_default_layernorm_scale_init_if_need(
-            self.scale_init, self.zero_centered_gamma
+            self.scale_init,
+            self.zero_centered_gamma,
         )
         super().__post_init__()
 
@@ -299,6 +296,7 @@ def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
         outputs : jax.numpy.ndarray
             Output tensors.
         """
+        x = x.astype(self.dtype)
 
         features = x.shape[-1]
         scale, ln_bias = _create_layernorm_parameters(
@@ -424,7 +422,9 @@ class DenseGeneral(TransformerEngineBase):
 
     def __post_init__(self):
         if self.kernel_init is None:
-            self.kernel_init = nn.initializers.variance_scaling(1.0, "fan_in", "truncated_normal")
+            self.kernel_init = nn.initializers.variance_scaling(
+                1.0, "fan_in", "truncated_normal", dtype=self.dtype
+            )
         super().__post_init__()
 
     @nn.compact
@@ -452,14 +452,13 @@ def __call__(self, inputs: Array) -> Array:
         kernel_shape = tuple(inputs.shape[ax] for ax in axis) + features
         kernel_param_shape = (np.prod([inputs.shape[ax] for ax in axis]),) + features
         kernel = nn_partitioning.param_with_axes(
-            "kernel", self.kernel_init, kernel_param_shape, jnp.float32, axes=self.kernel_axes
+            "kernel", self.kernel_init, kernel_shape, self.dtype, axes=self.kernel_axes
         )
-
-        kernel = jnp.reshape(kernel, kernel_shape)
+        kernel = kernel.astype(self.dtype)
 
         if self.use_bias:
             bias = nn_partitioning.param_with_axes(
-                "bias", self.bias_init, features, jnp.float32, axes=self.bias_axes
+                "bias", self.bias_init, features, self.dtype, axes=self.bias_axes
             )
             bias = bias.astype(self.dtype)
         else:
@@ -490,7 +489,7 @@ def __call__(self, inputs: Array) -> Array:
                 "lora_a_kernel",
                 self.kernel_init,
                 lora_a_kernel_init_shape,
-                jnp.float32,
+                self.dtype,
                 axes=lora_a_kernel_axes,
             )
             lora_a_kernel = jnp.reshape(lora_a_kernel, lora_a_kernel_shape)
@@ -502,7 +501,7 @@ def __call__(self, inputs: Array) -> Array:
                 "lora_b_kernel",
                 nn.initializers.zeros,
                 lora_b_kernel_shape,
-                jnp.float32,
+                self.dtype,
                 axes=lora_b_kernel_axes,
             )
             lora_b_kernel = lora_b_kernel.astype(self.dtype)
@@ -633,9 +632,12 @@ class LayerNormDenseGeneral(TransformerEngineBase):
 
     def __post_init__(self):
         if self.kernel_init is None:
-            self.kernel_init = nn.initializers.variance_scaling(1.0, "fan_in", "truncated_normal")
+            self.kernel_init = nn.initializers.variance_scaling(
+                1.0, "fan_in", "truncated_normal", dtype=self.dtype
+            )
         self.scale_init = _obtain_default_layernorm_scale_init_if_need(
-            self.scale_init, self.zero_centered_gamma
+            self.scale_init,
+            self.zero_centered_gamma,
         )
         super().__post_init__()
 
@@ -665,6 +667,7 @@ def __call__(self, inputs: Array) -> Array:
             and not self.return_layernorm_output
             and self.enable_layernorm
         )
+        inputs = inputs.astype(self.dtype)
 
         if self.enable_layernorm:
             inputs = with_sharding_constraint_by_logical_axes(inputs, self.layernorm_input_axes)
@@ -709,10 +712,9 @@ def __call__(self, inputs: Array) -> Array:
         kernel_shape = tuple(y.shape[ax] for ax in axis) + features
         kernel_param_shape = (np.prod([inputs.shape[ax] for ax in axis]),) + features
         kernel = nn_partitioning.param_with_axes(
-            "kernel", self.kernel_init, kernel_param_shape, jnp.float32, axes=self.kernel_axes
+            "kernel", self.kernel_init, kernel_shape, self.dtype, axes=self.kernel_axes
         )
-
-        kernel = jnp.reshape(kernel, kernel_shape)
+        kernel = kernel.astype(self.dtype)
 
         contract_ind = tuple(range(0, len(axis)))
 
@@ -755,7 +757,7 @@ def __call__(self, inputs: Array) -> Array:
                 "lora_a_kernel",
                 self.kernel_init,
                 lora_a_kernel_init_shape,
-                jnp.float32,
+                self.dtype,
                 axes=lora_a_kernel_axes,
             )
             lora_a_kernel = jnp.reshape(lora_a_kernel, lora_a_kernel_shape)
@@ -767,7 +769,7 @@ def __call__(self, inputs: Array) -> Array:
                 "lora_b_kernel",
                 nn.initializers.zeros,
                 lora_b_kernel_shape,
-                jnp.float32,
+                self.dtype,
                 axes=lora_b_kernel_axes,
             )
             lora_b_kernel = lora_b_kernel.astype(self.dtype)
@@ -779,7 +781,7 @@ def __call__(self, inputs: Array) -> Array:
         bias = None
         if self.use_bias:
             bias = nn_partitioning.param_with_axes(
-                "bias", self.bias_init, features, jnp.float32, axes=self.bias_axes
+                "bias", self.bias_init, features, self.dtype, axes=self.bias_axes
             )
             bias = bias.astype(self.dtype)
 
@@ -935,9 +937,12 @@ class LayerNormMLP(TransformerEngineBase):
 
     def __post_init__(self):
         if self.kernel_init is None:
-            self.kernel_init = nn.initializers.variance_scaling(1.0, "fan_in", "truncated_normal")
+            self.kernel_init = nn.initializers.variance_scaling(
+                1.0, "fan_in", "truncated_normal", dtype=self.dtype
+            )
         self.scale_init = _obtain_default_layernorm_scale_init_if_need(
-            self.scale_init, self.zero_centered_gamma
+            self.scale_init,
+            self.zero_centered_gamma,
         )
         super().__post_init__()
 
@@ -970,6 +975,8 @@ def __call__(self, inputs: Array, deterministic: bool = False) -> Array:
             and self.enable_layernorm
         )
 
+        inputs = inputs.astype(self.dtype)
+
         gated_act_pool = [
             ("gelu", "linear"),
             ("silu", "linear"),
@@ -1033,7 +1040,7 @@ def kernel_1_init(key, num_kernels, stack_axis, *init_args):
             for _ in range(num_kernels):
                 key, init_key = jax_random.split(key)
                 kernels.append(self.kernel_init(init_key, *init_args))
-            return jnp.stack(kernels, axis=stack_axis, dtype=jnp.float32)
+            return jnp.stack(kernels, axis=stack_axis, dtype=self.dtype)
 
         wi_fp8_meta_pkg = None
         wo_fp8_meta_pkg = None
@@ -1054,10 +1061,11 @@ def kernel_1_init(key, num_kernels, stack_axis, *init_args):
             num_activations,
             -2,
             kernel_1_each_shape,
-            jnp.float32,
+            self.dtype,
             axes=self.kernel_axes_1,
         )
         kernel_1 = jnp.reshape(kernel_1, kernel_1_shape)
+        kernel_1 = kernel_1.astype(self.dtype)
         hidden_size = inputs.shape[-1]
         hidden_size_tuple = _canonicalize_tuple(hidden_size)
         kernel_2_shape = (self.intermediate_dim,) + hidden_size_tuple
@@ -1066,10 +1074,11 @@ def kernel_1_init(key, num_kernels, stack_axis, *init_args):
             "wo_kernel",
             self.kernel_init,
             kernel_2_param_shape,
-            jnp.float32,
+            self.dtype,
             axes=self.kernel_axes_2,
         )
         kernel_2 = jnp.reshape(kernel_2, kernel_2_shape)
+        kernel_2 = kernel_2.astype(self.dtype)
         contract_ind = tuple(range(0, len(axis)))
 
         ffn1_ckpt_name = "ffn1"
@@ -1081,13 +1090,13 @@ def kernel_1_init(key, num_kernels, stack_axis, *init_args):
             if self.use_bias:
                 bias_1_shape = intermediate_dim
                 bias_1 = nn_partitioning.param_with_axes(
-                    "wi_bias", self.bias_init, bias_1_shape, jnp.float32, axes=self.bias_axes_1
+                    "wi_bias", self.bias_init, bias_1_shape, self.dtype, axes=self.bias_axes_1
                 )
                 bias_1 = bias_1.astype(self.dtype)
 
                 bias_2_shape = (hidden_size,)
                 bias_2 = nn_partitioning.param_with_axes(
-                    "wo_bias", self.bias_init, bias_2_shape, jnp.float32, axes=self.bias_axes_2
+                    "wo_bias", self.bias_init, bias_2_shape, self.dtype, axes=self.bias_axes_2
                 )
                 bias_2 = bias_2.astype(self.dtype)
             else:
@@ -1156,7 +1165,7 @@ def kernel_1_init(key, num_kernels, stack_axis, *init_args):
                     num_activations,
                     -2,
                     wi_lora_a_kernel_init_each_shape,
-                    jnp.float32,
+                    self.dtype,
                     axes=wi_lora_a_kernel_axes,
                 )
                 wi_lora_a_kernel = jnp.reshape(wi_lora_a_kernel, wi_lora_a_kernel_shape)
@@ -1172,7 +1181,7 @@ def kernel_1_init(key, num_kernels, stack_axis, *init_args):
                     "wi_lora_b_kernel",
                     nn.initializers.zeros,
                     wi_lora_b_kernel_shape,
-                    jnp.float32,
+                    self.dtype,
                     axes=wi_lora_b_kernel_axes,
                 )
                 wi_lora_b_kernel = wi_lora_b_kernel.astype(self.dtype)
@@ -1189,10 +1198,10 @@ def kernel_1_init(key, num_kernels, stack_axis, *init_args):
             bias_1 = None
             if self.use_bias:
                 bias_1 = nn_partitioning.param_with_axes(
-                    "wi_bias", self.bias_init, intermediate_dim, jnp.float32, axes=self.bias_axes_1
+                    "wi_bias", self.bias_init, intermediate_dim, self.dtype, axes=self.bias_axes_1
                 )
-                bias_1 = bias_1.astype(self.dtype)
                 bias_1_shape = (1,) * (x.ndim - bias_1.ndim) + bias_1.shape
+                bias_1 = bias_1.astype(self.dtype)
                 x += jnp.reshape(bias_1, bias_1_shape)
 
             x = checkpoint_name(x, ffn1_ckpt_name)
@@ -1207,6 +1216,8 @@ def kernel_1_init(key, num_kernels, stack_axis, *init_args):
                 z = functools.reduce(operator.mul, activations)
                 # Remove act axis
                 z = jnp.reshape(z, (*z.shape[:-2], -1))
+            z = z.astype(self.dtype)
+            # import pdb; pdb.set_trace()
 
             z = nn.Dropout(
                 rate=self.intermediate_dropout_rate,
@@ -1215,6 +1226,7 @@ def kernel_1_init(key, num_kernels, stack_axis, *init_args):
             )(z, deterministic=deterministic)
 
             z = with_sharding_constraint_by_logical_axes(z, self.dot_2_input_axes)
+            z = z.astype(self.dtype)
 
             # DenseGeneral 2
             out = type_safe_dot_general(
@@ -1228,7 +1240,7 @@ def kernel_1_init(key, num_kernels, stack_axis, *init_args):
                     "wo_lora_a_kernel",
                     self.kernel_init,
                     wo_lora_a_kernel_shape,
-                    jnp.float32,
+                    self.dtype,
                     axes=wo_lora_a_kernel_axes,
                 )
                 wo_lora_a_kernel = wo_lora_a_kernel.astype(self.dtype)
@@ -1239,7 +1251,7 @@ def kernel_1_init(key, num_kernels, stack_axis, *init_args):
                     "wo_lora_b_kernel",
                     nn.initializers.zeros,
                     wo_lora_b_kernel_shape,
-                    jnp.float32,
+                    self.dtype,
                     axes=wo_lora_b_kernel_axes,
                 )
                 wo_lora_b_kernel = wo_lora_b_kernel.astype(self.dtype)
@@ -1256,7 +1268,7 @@ def kernel_1_init(key, num_kernels, stack_axis, *init_args):
             bias_2 = None
             if self.use_bias:
                 bias_2 = nn_partitioning.param_with_axes(
-                    "wo_bias", self.bias_init, (hidden_size,), jnp.float32, axes=self.bias_axes_2
+                    "wo_bias", self.bias_init, (hidden_size,), self.dtype, axes=self.bias_axes_2
                 )
                 bias_2 = bias_2.astype(self.dtype)
                 out += jnp.reshape(bias_2, (1,) * (out.ndim - 1) + (-1,))

transformer_engine/jax/flax/transformer.py

@@ -976,7 +976,9 @@ def __post_init__(self):
         )
 
         if self.kernel_init is None:
-            self.kernel_init = nn.initializers.variance_scaling(1.0, "fan_in", "normal")
+            self.kernel_init = nn.initializers.variance_scaling(
+                1.0, "fan_in", "normal", dtype=self.dtype
+            )
         if self.num_gqa_groups is None:
             self.num_gqa_groups = self.num_attention_heads
         super().__post_init__()
@@ -1198,6 +1200,7 @@ def generate_batch_seqlen_logical_axes(is_sharded_seq):
                 inputs_kv = ln_out
 
             key = kv_projection(kernel_init=self.kernel_init, name="key")(inputs_kv)
+            key = key.astype(self.dtype)
             value = kv_projection(kernel_init=self.kernel_init, name="value")(inputs_kv)
             query = checkpoint_name(query, "query_proj")
             key = checkpoint_name(key, "key_proj")
@@ -1437,7 +1440,7 @@ def __call__(self, q_seqlen, k_seqlen, bidirectional=True):
             "rel_embedding",
             self.embedding_init,
             (self.num_attention_heads, self.num_buckets),
-            jnp.float32,
+            self.dtype,
             axes=self.embedding_axes,
         )
 
@@ -1673,10 +1676,12 @@ class TransformerLayer(nn.Module):  # pylint: disable=too-few-public-methods
 
     def __post_init__(self):
         if self.mha_kernel_init is None:
-            self.mha_kernel_init = nn.initializers.variance_scaling(1.0, "fan_in", "normal")
+            self.mha_kernel_init = nn.initializers.variance_scaling(
+                1.0, "fan_in", "normal", dtype=self.dtype
+            )
         if self.mlp_kernel_init is None:
             self.mlp_kernel_init = nn.initializers.variance_scaling(
-                1.0, "fan_in", "truncated_normal"
+                1.0, "fan_in", "truncated_normal", dtype=self.dtype
             )
         if self.num_gqa_groups is None:
             self.num_gqa_groups = self.num_attention_heads
@@ -1726,6 +1731,9 @@ def __call__(
         outputs: jax.numpy.ndarray
             Output tensors.
         """
+
+        inputs = inputs.astype(self.dtype)
+
         assert (
             self.layer_type in TransformerLayerType
         ), f"layer_type should be one of TransformerLayerType, but got {self.layer_type}."