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feat(quantization): support MOE_REQUANTIZE_WEIGHT_DTYPE and MOE_REQUANTIZE_BLOCK_SIZE in MXFP4 MoE #3494

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feat(quantization): support MOE_REQUANTIZE_WEIGHT_DTYPE and MOE_REQUANTIZE_BLOCK_SIZE in MXFP4 MoE #3494

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8 changes: 8 additions & 0 deletions tpu_inference/envs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,6 +83,8 @@
VLLM_TPU_BUCKET_PADDING_GAP: int = 0
VLLM_INCREMENTAL_FP8_LOADING: bool = False
TPU_MESH_SORT_BY_COORDS: bool = False
MOE_REQUANTIZE_WEIGHT_DTYPE: str = ""
MOE_REQUANTIZE_BLOCK_SIZE: str = ""


def env_with_choices(
Expand Down Expand Up @@ -494,6 +496,12 @@ def _get_int_list_env() -> list[int]:
# when initializing large FP8 models on smaller RAM TPUs such as TPU8i.
"VLLM_INCREMENTAL_FP8_LOADING":
env_bool("VLLM_INCREMENTAL_FP8_LOADING", default=False),
# Controls the target quantization dtype for MoE requantization (e.g. 'fp8', 'int4').
"MOE_REQUANTIZE_WEIGHT_DTYPE":
lambda: os.getenv("MOE_REQUANTIZE_WEIGHT_DTYPE", ""),
# Controls the block size for MoE requantization (e.g. '32', '64').
"MOE_REQUANTIZE_BLOCK_SIZE":
lambda: os.getenv("MOE_REQUANTIZE_BLOCK_SIZE", ""),
}


Expand Down
106 changes: 66 additions & 40 deletions tpu_inference/layers/vllm/quantization/mxfp4.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.

import functools
from typing import Optional

import jax
Expand All @@ -38,6 +39,7 @@
from vllm.model_executor.layers.quantization.utils.quant_utils import \
is_layer_skipped

from tpu_inference import envs
from tpu_inference.layers.common.moe import \
FusedMoEMethodBase as TpuFusedMoEMethodBase
from tpu_inference.layers.common.process_weights.moe_weights import (
Expand All @@ -53,7 +55,7 @@
from tpu_inference.layers.vllm.quantization.unquantized import \
VllmUnquantizedLinearMethod
from tpu_inference.logger import init_logger
from tpu_inference.utils import get_mesh_shape_product, t2j
from tpu_inference.utils import get_mesh_shape_product, t2j, to_jax_dtype

P = PartitionSpec

Expand Down Expand Up @@ -91,6 +93,56 @@ def get_quant_method(self, layer: torch.nn.Module,
return None


@functools.partial(
jax.jit,
static_argnames=(
"moe_backend",
"w13_reorder_size",
"w13_interleave",
"desired_quant_dtype",
"requant_block_size",
),
)
def _process_mxfp4_moe_weights(
w13_weight: jax.Array,
w13_weight_scale: jax.Array,
w13_bias: jax.Array | None,
w2_weight: jax.Array,
w2_weight_scale: jax.Array,
w2_bias: jax.Array | None,
moe_backend: Any,
w13_reorder_size: int,
w13_interleave: bool,
desired_quant_dtype: Any,
requant_block_size: int,
) -> FusedMoEWeights:
# Dequantize fp4 weights into fp32.
w13_weight = dequantize_tensor_from_mxfp4_packed(
w13_weight, w13_weight_scale, 2, jnp.float32)
w2_weight = dequantize_tensor_from_mxfp4_packed(
w2_weight, w2_weight_scale, 2, jnp.float32)

weights = quantize_moe_weights(
FusedMoEWeights(
w13_weight=w13_weight,
w13_weight_scale=None,
w13_bias=w13_bias,
w2_weight=w2_weight,
w2_weight_scale=None,
w2_bias=w2_bias,
),
desired_quant_dtype,
requant_block_size,
w13_interleave=w13_interleave,
)
return process_moe_weights(
weights,
moe_backend=moe_backend,
w13_reorder_size=w13_reorder_size,
w13_interleave=w13_interleave,
)


class VllmMxfp4MoEMethod(Mxfp4MoEMethod, FusedMoEMethodBase):

def __init__(
Expand Down Expand Up @@ -141,51 +193,25 @@ def process_weights_after_loading(self, layer: torch.nn.Module):
w2_weight_scale = t2j(layer.w2_weight_scale, use_dlpack=False)
w2_bias = t2j(layer.w2_bias, use_dlpack=False) if has_bias else None

@jax.jit
def process_mxfp4_moe_weights(
w13_weight: jax.Array,
w13_weight_scale: jax.Array,
w13_bias: jax.Array | None,
w2_weight: jax.Array,
w2_weight_scale: jax.Array,
w2_bias: jax.Array | None,
) -> FusedMoEWeights:
# Dequantize fp4 weights into fp32.
w13_weight = dequantize_tensor_from_mxfp4_packed(
w13_weight, w13_weight_scale, 2, jnp.float32)
w2_weight = dequantize_tensor_from_mxfp4_packed(
w2_weight, w2_weight_scale, 2, jnp.float32)
w13_interleave = layer.activation == MoEActivation.SWIGLUOAI
w13_reorder_size = get_mesh_shape_product(
self.mesh, ShardingAxisName.MLP_TENSOR)

weights = quantize_moe_weights(
FusedMoEWeights(
w13_weight=w13_weight,
w13_weight_scale=None,
w13_bias=w13_bias,
w2_weight=w2_weight,
w2_weight_scale=None,
w2_bias=w2_bias,
),
jnp.float4_e2m1fn,
MXFP4_REQUANTIZED_BLOCK_SIZE,
w13_interleave=w13_interleave,
)
return process_moe_weights(
weights,
moe_backend=self.moe_backend,
w13_reorder_size=w13_reorder_size,
w13_interleave=w13_interleave,
)

weights = process_mxfp4_moe_weights(
w13_interleave = layer.activation == MoEActivation.SWIGLUOAI
w13_reorder_size = get_mesh_shape_product(
self.mesh, ShardingAxisName.MLP_TENSOR)

desired_quant_dtype = to_jax_dtype(envs.MOE_REQUANTIZE_WEIGHT_DTYPE) if envs.MOE_REQUANTIZE_WEIGHT_DTYPE else jnp.float4_e2m1fn
requant_block_size = int(envs.MOE_REQUANTIZE_BLOCK_SIZE) if envs.MOE_REQUANTIZE_BLOCK_SIZE else MXFP4_REQUANTIZED_BLOCK_SIZE

weights = _process_mxfp4_moe_weights(
w13_weight,
w13_weight_scale,
w13_bias,
w2_weight,
w2_weight_scale,
w2_bias,
moe_backend=self.moe_backend,
w13_reorder_size=w13_reorder_size,
w13_interleave=w13_interleave,
desired_quant_dtype=desired_quant_dtype,
requant_block_size=requant_block_size,
)
weights = torch_view(
shard_moe_weights(weights, self.moe_backend, self.mesh))
Expand Down