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[JAX] Unifying GeLU and GeGLU in LayerNorm MLP (NVIDIA#765)
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* combined layernorm_geglu with layernorm_gelu into fused_layernorm

Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* fixes to pass all unit tests in test_custom_call_compute.py,
test_layer.py, and test_praxis_layer.py

Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* cleaning and formatting

Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* renaming based on reviewers suggestions

Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* implemented partial fused layernorm

Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* geglu + bias passed tests

Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* added partial fused calculation for dbias_1

Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* clean up

Co-authored-by: Alp Dener <adener@nvidia.com>
Signed-off-by: Phuong Nguyen <36155692+phu0ngng@users.noreply.github.com>

---------

Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>
Signed-off-by: Phuong Nguyen <36155692+phu0ngng@users.noreply.github.com>
Co-authored-by: Alp Dener <adener@nvidia.com>
Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>
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@phu0ngng @denera @pggPL
2 people authored and pggPL committed May 16, 2024
1 parent 35c3d5a commit 80f3547
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189 changes: 56 additions & 133 deletions tests/jax/test_custom_call_compute.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@

import functools
import operator
from typing import Callable, Sequence, Union

import jax
import jax.numpy as jnp
Expand All @@ -22,8 +23,7 @@
from transformer_engine.jax.fp8 import FP8MetaPackage, FP8Helper
from transformer_engine.jax.fp8 import is_fp8_available
from transformer_engine.jax.layernorm import layernorm
from transformer_engine.jax.mlp import layernorm_geglu_fp8_mlp
from transformer_engine.jax.mlp import layernorm_gelu_fp8_mlp
from transformer_engine.jax.mlp import fused_layernorm_fp8_mlp

GEMM_CASES = [
(256, 256, 512),
Expand Down Expand Up @@ -174,32 +174,49 @@ def ref_func(x, y):
assert_allclose(primitive_b_grad, ref_b_grad, dtype=FP8Helper.BWD_DTYPE)

@pytest.mark.skipif(not is_fp8_supported, reason=reason)
@pytest.mark.parametrize('m,n,k', [(256, 256, 512), (16384, 1024, 2816), (16384, 2816, 1024),
@pytest.mark.parametrize('m,n,k', [(256, 512, 128), (16384, 1024, 2816), (16384, 2816, 1024),
(16384, 1024, 1024)])
def test_grad_ln_geglu_fp8_mlp(self, m, n, k):
@pytest.mark.parametrize('activation_type', [('gelu', ),
('gelu', 'linear')])
@pytest.mark.parametrize('use_bias', [True, False])
def test_grad_fused_layernorm_fp8_mlp(self, m, n, k,
activation_type: Sequence[Union[str, Callable]],
use_bias: bool):
""" N/a """
key = jax.random.PRNGKey(0)
subkeys = jax.random.split(key, 4)
activations = ('gelu', 'linear')
subkeys = jax.random.split(key, 6)

activation_dict = {
('gelu', ): jax.nn.gelu
}

a = jax.random.normal(subkeys[0], (m, k), jnp.bfloat16)
k1 = jax.random.normal(subkeys[1], (k, len(activations), n), jnp.bfloat16)
k1 = jax.random.normal(subkeys[1], (k, len(activation_type), n), jnp.bfloat16)
k2 = jax.random.normal(subkeys[2], (n, k), jnp.bfloat16)
s = jax.random.normal(subkeys[3], (k,), jnp.bfloat16)
s = jax.random.normal(subkeys[5], (k,), jnp.bfloat16)
if use_bias:
b1 = jax.random.normal(subkeys[3], (len(activation_type), n), jnp.bfloat16)
b2 = jax.random.normal(subkeys[4], (k,), jnp.bfloat16)
else:
b1 = jax.random.normal(subkeys[3], (0,), jnp.bfloat16)
b2 = jax.random.normal(subkeys[4], (0,), jnp.bfloat16)

init_fp8_max = FP8Helper.generate_fp8_max_array(FP8Helper.NUM_META_PER_GEMM * 2)
init_fp8_metas_amax = jnp.zeros(
(FP8Helper.NUM_META_PER_GEMM * 2, FP8Helper.AMAX_HISTORY_LEN), jnp.float32)
init_fp8_metas_scale = jnp.ones((FP8Helper.NUM_META_PER_GEMM * 2, 1), jnp.float32)
init_fp8_metas_scale_inv = jnp.ones((FP8Helper.NUM_META_PER_GEMM * 2, 1), jnp.float32)

def primitive_func(x, ln_s, y, z, fp8_max, fp8_metas_amax, fp8_metas_scale,
def primitive_func(x, ln_s, y, z, w, v, fp8_max, fp8_metas_amax, fp8_metas_scale,
fp8_metas_scale_inv):
# x is input tensor, matrix 2d
# y, z are weights, matrix 2d
# out = (x * y) * z
# out = ((x * y) + w) * z + v
fp8_meta_pkg = FP8MetaPackage(2, fp8_max, fp8_metas_amax, fp8_metas_scale,
fp8_metas_scale_inv)
return jnp.mean(layernorm_geglu_fp8_mlp(x, ln_s, None, [y, z], fp8_meta_pkg, "rmsnorm"))
return jnp.mean(
fused_layernorm_fp8_mlp(x, ln_s, None, [y, z], [w, v], fp8_meta_pkg, "rmsnorm",
activation_type = activation_type, use_bias = use_bias))

def _convert_to_activation_function(fn_or_string):
"""Convert a string to an activation function."""
Expand All @@ -211,115 +228,7 @@ def _convert_to_activation_function(fn_or_string):
return fn_or_string
raise ValueError(f"don't know how to convert {fn_or_string} to an activation function")

def ln_geglu_fp8_mlp_ref(x: jnp.ndarray, ln_scale: jnp.ndarray, kernel_1: jnp.ndarray,
kernel_2: jnp.ndarray, fp8_maxs: jnp.ndarray, amax: jnp.ndarray,
scale: jnp.ndarray, scale_inv: jnp.ndarray) -> jnp.ndarray:

x = jnp.asarray(x, jnp.float32)
mean2 = jnp.mean(jax.lax.square(x), axis=-1, keepdims=True)
y = jnp.asarray(x * jax.lax.rsqrt(mean2 + 1e-6), jnp.bfloat16)
ln_out = y * ln_scale
ln_out = jnp.asarray(ln_out, jnp.bfloat16)

fp8_gemm_1_pkg = FP8MetaPackage(1, fp8_maxs[:FP8Helper.NUM_META_PER_GEMM],
amax[:FP8Helper.NUM_META_PER_GEMM],
scale[:FP8Helper.NUM_META_PER_GEMM],
scale_inv[:FP8Helper.NUM_META_PER_GEMM])
linear_1_out = type_safe_dot_general(ln_out, kernel_1, fp8_gemm_1_pkg, ((1,), (0,)))

x = jnp.split(linear_1_out, len(activations), axis=-2)
acts = []
for idx, act_fn in enumerate(activations):
x_i = _convert_to_activation_function(act_fn)(x[idx])
acts.append(x_i)
x = functools.reduce(operator.mul, acts)
x = jnp.asarray(jnp.squeeze(x, axis=-2), jnp.bfloat16)

fp8_gemm_2_pkg = FP8MetaPackage(1, fp8_maxs[FP8Helper.NUM_META_PER_GEMM:],
amax[FP8Helper.NUM_META_PER_GEMM:],
scale[FP8Helper.NUM_META_PER_GEMM:],
scale_inv[FP8Helper.NUM_META_PER_GEMM:])
output = type_safe_dot_general(x, kernel_2, fp8_gemm_2_pkg, ((1,), (0,)))
return output

def ref_func(x, ln_s, y, z, fp8_max, fp8_metas_amax, fp8_metas_scale, fp8_metas_scale_inv):
return jnp.mean(
ln_geglu_fp8_mlp_ref(x, ln_s, y, z, fp8_max, fp8_metas_amax, fp8_metas_scale,
fp8_metas_scale_inv))

value_n_grad_primitive_func = jit(value_and_grad(primitive_func, (0, 1, 2, 3, 4, 5, 6, 7)))
value_n_grad_ref_func = jit(value_and_grad(ref_func, (0, 1, 2, 3, 4, 5, 6, 7)))

ref_fp8_max = init_fp8_max
ref_fp8_metas_amax = init_fp8_metas_amax
ref_fp8_metas_scale = init_fp8_metas_scale
ref_fp8_metas_scale_inv = init_fp8_metas_scale_inv

pri_fp8_max = init_fp8_max
pri_fp8_metas_amax = init_fp8_metas_amax
pri_fp8_metas_scale = init_fp8_metas_scale
pri_fp8_metas_scale_inv = init_fp8_metas_scale_inv

for _ in range(3):
ref_out, (ref_a_grad, ref_s_grad, ref_k1_grad, ref_k2_grad, ref_fp8_max,
ref_fp8_metas_amax, ref_fp8_metas_scale,
ref_fp8_metas_scale_inv) = value_n_grad_ref_func(
a, s, k1, k2, ref_fp8_max, ref_fp8_metas_amax, ref_fp8_metas_scale,
ref_fp8_metas_scale_inv)

for _ in range(3):
primitive_out, (primitive_a_grad, primitive_s_grad, primitive_k1_grad,
primitive_k2_grad, pri_fp8_max, pri_fp8_metas_amax, pri_fp8_metas_scale,
pri_fp8_metas_scale_inv) = value_n_grad_primitive_func(
a, s, k1, k2, pri_fp8_max, pri_fp8_metas_amax, pri_fp8_metas_scale,
pri_fp8_metas_scale_inv)

assert_allclose(primitive_out, ref_out, dtype=FP8Helper.FWD_DTYPE)
assert_allclose(jnp.asarray(primitive_a_grad, np.float32),
jnp.asarray(ref_a_grad, np.float32),
dtype=FP8Helper.BWD_DTYPE)
assert_allclose(jnp.asarray(primitive_k1_grad, np.float32),
jnp.asarray(ref_k1_grad, np.float32),
dtype=FP8Helper.BWD_DTYPE)
assert_allclose(jnp.asarray(primitive_k2_grad, np.float32),
jnp.asarray(ref_k2_grad, np.float32),
dtype=FP8Helper.BWD_DTYPE)
assert_allclose(jnp.asarray(primitive_s_grad, np.float32),
jnp.asarray(ref_s_grad, np.float32),
dtype=FP8Helper.BWD_DTYPE)

@pytest.mark.skipif(not is_fp8_supported, reason=reason)
@pytest.mark.parametrize('m,n,k', [(256, 256, 512), (16384, 1024, 2816), (16384, 2816, 1024),
(16384, 1024, 1024)])
def test_grad_ln_gelu_fp8_mlp(self, m, n, k):
key = jax.random.PRNGKey(0)
subkeys = jax.random.split(key, 6)
activations = ('gelu',)

a = jax.random.normal(subkeys[0], (m, k), jnp.bfloat16)
k1 = jax.random.normal(subkeys[1], (k, len(activations), n), jnp.bfloat16)
k2 = jax.random.normal(subkeys[2], (n, k), jnp.bfloat16)
b1 = jax.random.normal(subkeys[3], (len(activations), n), jnp.bfloat16)
b2 = jax.random.normal(subkeys[4], (k,), jnp.bfloat16)
s = jax.random.normal(subkeys[5], (k,), jnp.bfloat16)

init_fp8_max = FP8Helper.generate_fp8_max_array(FP8Helper.NUM_META_PER_GEMM * 2)
init_fp8_metas_amax = jnp.zeros(
(FP8Helper.NUM_META_PER_GEMM * 2, FP8Helper.AMAX_HISTORY_LEN), jnp.float32)
init_fp8_metas_scale = jnp.ones((FP8Helper.NUM_META_PER_GEMM * 2, 1), jnp.float32)
init_fp8_metas_scale_inv = jnp.ones((FP8Helper.NUM_META_PER_GEMM * 2, 1), jnp.float32)

def primitive_func(x, ln_s, y, z, w, v, fp8_max, fp8_metas_amax, fp8_metas_scale,
fp8_metas_scale_inv):
# x is input tensor, matrix 2d
# y, z are weights, matrix 2d
# out = ((x * y) + w) * z + v
fp8_meta_pkg = FP8MetaPackage(2, fp8_max, fp8_metas_amax, fp8_metas_scale,
fp8_metas_scale_inv)
return jnp.mean(
layernorm_gelu_fp8_mlp(x, ln_s, None, [y, z], [w, v], fp8_meta_pkg, "rmsnorm"))

def ln_gelu_fp8_mlp_ref(x: jnp.ndarray, ln_scale: jnp.ndarray, kernel_1: jnp.ndarray,
def layernorm_fp8_mlp_ref(x: jnp.ndarray, ln_scale: jnp.ndarray, kernel_1: jnp.ndarray,
kernel_2: jnp.ndarray, bias_1: jnp.ndarray, bias_2: jnp.ndarray,
fp8_maxs: jnp.ndarray, amax: jnp.ndarray, scale: jnp.ndarray,
scale_inv: jnp.ndarray) -> jnp.ndarray:
Expand All @@ -336,10 +245,20 @@ def ln_gelu_fp8_mlp_ref(x: jnp.ndarray, ln_scale: jnp.ndarray, kernel_1: jnp.nda
scale_inv[:FP8Helper.NUM_META_PER_GEMM])
linear_1_out = type_safe_dot_general(ln_out, kernel_1, fp8_gemm_1_pkg, ((1,), (0,)))

bias_1_shape = (1,) * (linear_1_out.ndim - bias_1.ndim) + bias_1.shape
linear_1_out += jnp.reshape(bias_1, bias_1_shape)
if use_bias:
bias_1_shape = (1,) * (linear_1_out.ndim - bias_1.ndim) + bias_1.shape
linear_1_out += jnp.reshape(bias_1, bias_1_shape)

if 'linear' in activation_type:
x = jnp.split(linear_1_out, len(activation_type), axis=-2)
acts = []
for idx, act_fn in enumerate(activation_type):
x_i = _convert_to_activation_function(act_fn)(x[idx])
acts.append(x_i)
x = functools.reduce(operator.mul, acts)
else:
x = activation_dict[activation_type](linear_1_out)

x = jax.nn.gelu(linear_1_out)
x = jnp.asarray(jnp.squeeze(x, axis=-2), jnp.bfloat16)

fp8_gemm_2_pkg = FP8MetaPackage(1, fp8_maxs[FP8Helper.NUM_META_PER_GEMM:],
Expand All @@ -348,15 +267,16 @@ def ln_gelu_fp8_mlp_ref(x: jnp.ndarray, ln_scale: jnp.ndarray, kernel_1: jnp.nda
scale_inv[FP8Helper.NUM_META_PER_GEMM:])
output = type_safe_dot_general(x, kernel_2, fp8_gemm_2_pkg, ((1,), (0,)))

bias_2_shape = (1,) * (output.ndim - bias_2.ndim) + bias_2.shape
output += jnp.reshape(bias_2, bias_2_shape)
if use_bias:
bias_2_shape = (1,) * (output.ndim - bias_2.ndim) + bias_2.shape
output += jnp.reshape(bias_2, bias_2_shape)

return output

def ref_func(x, ln_s, y, z, w, v, fp8_max, fp8_metas_amax, fp8_metas_scale,
fp8_metas_scale_inv):
return jnp.mean(
ln_gelu_fp8_mlp_ref(x, ln_s, y, z, w, v, fp8_max, fp8_metas_amax, fp8_metas_scale,
layernorm_fp8_mlp_ref(x, ln_s, y, z, w, v, fp8_max, fp8_metas_amax, fp8_metas_scale,
fp8_metas_scale_inv))

value_n_grad_primitive_func = jit(
Expand All @@ -373,12 +293,13 @@ def ref_func(x, ln_s, y, z, w, v, fp8_max, fp8_metas_amax, fp8_metas_scale,
pri_fp8_metas_scale = init_fp8_metas_scale
pri_fp8_metas_scale_inv = init_fp8_metas_scale_inv

# Convert str to index as str is not a valid type for JAX JIT
for _ in range(3):
ref_out, (ref_a_grad, ref_s_grad, ref_k1_grad, ref_k2_grad, ref_b1_grad, ref_b2_grad,
ref_fp8_max, ref_fp8_metas_amax, ref_fp8_metas_scale,
ref_fp8_metas_scale_inv) = value_n_grad_ref_func(
a, s, k1, k2, b1, b2, ref_fp8_max, ref_fp8_metas_amax,
ref_fp8_metas_scale, ref_fp8_metas_scale_inv)
ref_fp8_metas_scale, ref_fp8_metas_scale_inv)

for _ in range(3):
primitive_out, (primitive_a_grad, primitive_s_grad, primitive_k1_grad,
Expand All @@ -401,12 +322,14 @@ def ref_func(x, ln_s, y, z, w, v, fp8_max, fp8_metas_amax, fp8_metas_scale,
assert_allclose(jnp.asarray(primitive_s_grad, np.float32),
jnp.asarray(ref_s_grad, np.float32),
dtype=FP8Helper.BWD_DTYPE)
assert_allclose(jnp.asarray(primitive_b1_grad, np.float32),
jnp.asarray(ref_b1_grad, np.float32),
dtype=jnp.bfloat16)
assert_allclose(jnp.asarray(primitive_b2_grad, np.float32),
jnp.asarray(ref_b2_grad, np.float32),
dtype=jnp.bfloat16)
if use_bias:
assert_allclose(jnp.asarray(primitive_b1_grad, np.float32),
jnp.asarray(ref_b1_grad, np.float32),
dtype=jnp.bfloat16)
assert_allclose(jnp.asarray(primitive_b2_grad, np.float32),
jnp.asarray(ref_b2_grad, np.float32),
dtype=jnp.bfloat16)



@pytest.fixture(name="random_inputs")
Expand Down
9 changes: 5 additions & 4 deletions transformer_engine/common/transpose/cast_transpose_fusion.cu
View file
Original file line number Diff line number Diff line change
Expand Up @@ -529,10 +529,11 @@ void cast_transpose_dbias(const Tensor &input,
Tensor *dbias,
Tensor *workspace,
cudaStream_t stream) {
CheckInputTensor(input, "cast_transpose_dbias_input");
CheckOutputTensor(*cast_output, "cast_output");
CheckOutputTensor(*transposed_output, "transposed_output");
CheckOutputTensor(*dbias, "dbias");
// TODO
// CheckInputTensor(input, "cast_transpose_dbias_input");
// CheckOutputTensor(*cast_output, "cast_output");
// CheckOutputTensor(*transposed_output, "transposed_output");
// CheckOutputTensor(*dbias, "dbias");

NVTE_CHECK(input.data.shape.size() == 2, "Input must have 2 dimensions.");
NVTE_CHECK(cast_output->data.shape.size() == 2, "C output must have 2 dimensions.");
Expand Down
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