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[ROCm][Quantization] GPT_OSS in amd-quark format model loading and emulations (#29008)

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Signed-off-by: xuebwang-amd <xuebwang@amd.com>
Signed-off-by: Robert Shaw <robshaw@redhat.com>
Co-authored-by: Robert Shaw <robshaw@redhat.com>
Co-authored-by: Robert Shaw <114415538+robertgshaw2-redhat@users.noreply.github.com>
This commit is contained in:
xuebwang-amd
2026-02-10 23:08:05 +08:00
committed by GitHub
parent 599e4335a4
commit b129136c7a
13 changed files with 1094 additions and 213 deletions
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509
vllm/model_executor/models/gpt_oss.py
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@@ -1,6 +1,7 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Iterable
import typing
from collections.abc import Callable, Iterable
import torch
import torch.distributed as dist
@@ -25,13 +26,17 @@ from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import QKVParallelLinear, RowParallelLinear
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.quantization.utils.ocp_mx_utils import OCP_MX_BLOCK_SIZE
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.utils import rocm_unquantized_gemm
from vllm.model_executor.layers.vocab_parallel_embedding import (
ParallelLMHead,
VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.model_loader.weight_utils import (
default_weight_loader,
maybe_remap_kv_scale_name,
)
from vllm.model_executor.models.utils import sequence_parallel_chunk
from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors
@@ -98,6 +103,7 @@ class OAIAttention(nn.Module):
head_size=self.head_dim,
total_num_heads=self.num_attention_heads,
total_num_kv_heads=self.num_key_value_heads,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.qkv_proj",
)
@@ -105,6 +111,7 @@ class OAIAttention(nn.Module):
self.o_proj = RowParallelLinear(
input_size=self.num_attention_heads * self.head_dim,
output_size=self.hidden_size,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.o_proj",
)
@@ -306,6 +313,19 @@ class GptOssModel(nn.Module):
return x, aux_hidden_states
return x
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
# Params for weights, weight scales, activation scales
# (param_name, weight_name, expert_id, shard_id)
# NOTE: this is only used for quark.
return FusedMoE.make_expert_params_mapping(
self,
ckpt_gate_proj_name="w1",
ckpt_down_proj_name="w2",
ckpt_up_proj_name="w3",
num_experts=self.config.num_local_experts,
num_redundant_experts=0,
)
def _load_weights_mxfp4(
self,
ep_rank_end: int,
@@ -318,7 +338,6 @@ class GptOssModel(nn.Module):
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
mxfp4_block = 32
use_ep = self.parallel_config.enable_expert_parallel
num_experts = self.config.num_local_experts
@@ -333,9 +352,11 @@ class GptOssModel(nn.Module):
)
intermediate_size = self.config.intermediate_size
intermediate_size_block = intermediate_size // mxfp4_block
intermediate_size_block = intermediate_size // OCP_MX_BLOCK_SIZE
per_rank_intermediate_size_block = cdiv(intermediate_size_block, tp_size)
per_rank_intermediate_size = per_rank_intermediate_size_block * mxfp4_block
per_rank_intermediate_size = (
per_rank_intermediate_size_block * OCP_MX_BLOCK_SIZE
)
# Calculate common slicing bounds for current rank
tp_rank_start = tp_rank * per_rank_intermediate_size
@@ -370,7 +391,9 @@ class GptOssModel(nn.Module):
narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
else:
narrow_weight = weight[
..., tp_rank_start // mxfp4_block : tp_rank_end // mxfp4_block
...,
tp_rank_start // OCP_MX_BLOCK_SIZE : tp_rank_end
// OCP_MX_BLOCK_SIZE,
]
param = params_dict[name]
@@ -495,6 +518,449 @@ class GptOssModel(nn.Module):
loaded_params.add(name)
return loaded_params
def _load_weights_quark(
self,
ep_rank_end: int,
ep_rank_start: int,
heads_per_rank: int,
head_start: int,
weights: Iterable[tuple[str, torch.Tensor]],
stacked_params_mapping: list[tuple[str, ...]],
) -> set[str]:
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
use_ep = self.parallel_config.enable_expert_parallel
num_experts = self.config.num_local_experts
if use_ep:
tp_rank = get_tensor_model_parallel_rank()
tp_size = get_tensor_model_parallel_world_size()
else:
tp_size, tp_rank = FusedMoEParallelConfig.flatten_tp_across_dp_and_pcp(
tp_size=get_tensor_model_parallel_world_size(),
dp_size=get_dp_group().world_size,
dp_rank=get_dp_group().rank_in_group,
pcp_size=get_pcp_group().world_size,
pcp_rank=get_pcp_group().rank_in_group,
)
def _get_moe_weight_dtype(layer_id: int = 0) -> str | None:
"""Helper function to get MoE quantization weight dtype.
Args:
layer_id: Layer index to check (default 0, as all layers should
have the same quantization method)
Returns:
Weight dtype string (e.g., "mxfp4", "fp8") or None if not available
"""
if hasattr(self.layers[layer_id].mlp.experts.quant_method, "weight_dtype"):
return self.layers[layer_id].mlp.experts.quant_method.weight_dtype
return None
intermediate_size = self.config.intermediate_size
moe_weight_dtype = _get_moe_weight_dtype(layer_id=0)
if moe_weight_dtype == "mxfp4":
# MXFP4 requires OCP_MX_BLOCK_SIZE alignment
intermediate_size_block = intermediate_size // OCP_MX_BLOCK_SIZE
per_rank_intermediate_size_block = cdiv(intermediate_size_block, tp_size)
per_rank_intermediate_size = (
per_rank_intermediate_size_block * OCP_MX_BLOCK_SIZE
)
else:
# FP8 and other formats don't need alignment
per_rank_intermediate_size = cdiv(intermediate_size, tp_size)
tp_rank_start = tp_rank * per_rank_intermediate_size
tp_rank_end = min((tp_rank + 1) * per_rank_intermediate_size, intermediate_size)
expert_params_mapping = self.get_expert_mapping()
for name, loaded_weight in weights:
if is_pp_missing_parameter(name, self):
continue
layer_id, expert_id, fused_name = None, None, None
moe_quant_method = None
if "experts" in name:
parts = name.split(".")
ids = [s for s in parts if s.isdigit()]
# for amd-quark format that each expert is seperated
# need to extract the parameter name with experts fused.
# example model: amd/gpt-oss-20b-MoE-Quant-W-MXFP4-A-FP8-KV-FP8
if len(ids) == 2:
layer_id, expert_id = int(ids[0]), int(ids[-1])
parts.pop(len(parts) - 1 - parts[::-1].index(str(expert_id)))
fused_name = ".".join(parts)
# for openai mxfp4 format that all experts are combined
# no need to extract the parameter name with experts fused.
# models: openai/gpt-oss-20b, openai/gpt-oss-120b
elif len(ids) == 1:
layer_id, expert_id = int(ids[0]), None
fused_name = name
else:
raise NameError(
f"Layer {name} contains more than 2 numeric indices. This is "
"an unexpected condition. Please open an issue if encountered."
)
moe_quant_method = _get_moe_weight_dtype(layer_id=layer_id)
def kv_cache_scale_loader(
quant_config: QuantizationConfig,
name: str,
params_dict: dict[str, typing.Any],
weight: torch.Tensor,
default_weight_loader: Callable[..., None],
loaded_params: set[str],
) -> tuple[bool, set[str]]:
"""
Load KV cache output scales.
Returns:
Tuple of (bool, set):
- bool: True if KV-cache scale was loaded into loaded_params
- set: Updated set of loaded_params if True else the original set
"""
# load explicit cached KV output scale from quant_config
if quant_config is not None and (
scale_name := quant_config.get_cache_scale(name)
):
param = params_dict[scale_name]
weight_loader = getattr(
param, "weight_loader", default_weight_loader
)
if weight.numel() != 1:
raise ValueError(
f"KV cache scale '{scale_name}' is expected to be a "
f"scalar, but got a tensor of shape {weight.shape}."
)
# Ensure weight is a scalar before passing to loader.
weight_loader(param, weight.flatten()[0])
loaded_params.add(scale_name)
return True, loaded_params
return False, loaded_params
load_kv_cache_scale_completed, loaded_params = kv_cache_scale_loader(
self.quant_config,
name,
params_dict,
loaded_weight,
default_weight_loader,
loaded_params,
)
if load_kv_cache_scale_completed:
continue
if (
all(key in name for key in ["input_scale", "mlp.experts"])
and expert_id is not None
):
assert loaded_weight.numel() == 1
expert_data = params_dict[fused_name].data[expert_id]
expert_data.copy_(loaded_weight)
loaded_params.add(fused_name)
continue
# Unified handler for mxfp4 weights and scales
elif moe_quant_method == "mxfp4" and any(
name.endswith(suffix)
for suffix in [
".w13_weight_scale",
".w2_weight_scale",
".w13_weight",
".w2_weight",
]
):
is_w13 = ".w13_" in name
is_scale = "_scale" in name
# Reshape weight for mxfp4 if needed (not for scales)
if not is_scale and expert_id is None:
if is_w13:
if loaded_weight.dim() < 3:
raise ValueError(
f"Expected w13_weight to have at least 3 "
f"dimensions, got shape "
f"{loaded_weight.shape}"
)
if loaded_weight.shape[0] != num_experts:
raise ValueError(
f"Expected w13_weight first dimension to be "
f"{num_experts}, got "
f"{loaded_weight.shape[0]}"
)
loaded_weight = loaded_weight.view(
num_experts, 2 * intermediate_size, -1
).contiguous()
else:
if loaded_weight.dim() < 3:
raise ValueError(
f"Expected w2_weight to have at least 3 "
f"dimensions, got shape "
f"{loaded_weight.shape}"
)
if loaded_weight.shape[0] != num_experts:
raise ValueError(
f"Expected w2_weight first dimension to be "
f"{num_experts}, got "
f"{loaded_weight.shape[0]}"
)
loaded_weight = loaded_weight.view(
num_experts, -1, intermediate_size // 2
).contiguous()
if use_ep:
sliced_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
else:
if is_w13:
if expert_id is None:
sliced_weight = loaded_weight[
:, 2 * tp_rank_start : 2 * tp_rank_end, ...
]
else:
sliced_weight = loaded_weight[
2 * tp_rank_start : 2 * tp_rank_end, ...
]
else:
if is_scale:
sliced_weight = loaded_weight[
...,
tp_rank_start // OCP_MX_BLOCK_SIZE : tp_rank_end
// OCP_MX_BLOCK_SIZE,
]
else:
sliced_weight = loaded_weight[
..., tp_rank_start // 2 : tp_rank_end // 2
]
# NOTE(rob): because gpt-oss ckpt has "unique" structure with
# fused gate_up_proj fused on disk, we cannot use the existing
# weight loaders without added complexity, so just do the
# direct load here.
param = params_dict[fused_name]
expert_data = param.data[expert_id]
dim1 = sliced_weight.shape[0]
dim2 = sliced_weight.shape[1]
expert_data.data[:dim1, :dim2].copy_(sliced_weight)
loaded_params.add(fused_name)
continue
elif name.endswith(".w13_weight") and moe_quant_method == "fp8":
if use_ep:
narrow_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
else:
if expert_id is None:
narrow_weight = loaded_weight[
:, 2 * tp_rank_start : 2 * tp_rank_end, :
]
else:
narrow_weight = loaded_weight[
2 * tp_rank_start : 2 * tp_rank_end, :
]
assert fused_name is not None
param = params_dict[fused_name]
if expert_id is None:
param.data.copy_(narrow_weight)
else:
param.data[expert_id].copy_(narrow_weight)
loaded_params.add(fused_name)
continue
elif name.endswith(".w13_weight_scale") and moe_quant_method == "fp8":
assert fused_name is not None
param = params_dict[fused_name]
# Check if this is per-channel or per-tensor scale
if loaded_weight.numel() > 1 and loaded_weight.dim() == 1:
if use_ep:
narrow_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
else:
narrow_weight = loaded_weight[
2 * tp_rank_start : 2 * tp_rank_end
]
else:
narrow_weight = loaded_weight
if expert_id is None:
param.data.copy_(narrow_weight)
else:
param.data[expert_id].copy_(narrow_weight)
loaded_params.add(fused_name)
continue
elif name.endswith(".w13_input_scale") and moe_quant_method == "fp8":
assert fused_name is not None
param = params_dict[fused_name]
if expert_id is None:
param.data.copy_(loaded_weight)
else:
param.data[expert_id].copy_(loaded_weight)
loaded_params.add(fused_name)
continue
elif name.endswith(".w2_weight") and moe_quant_method == "fp8":
if use_ep:
narrow_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
else:
if expert_id is None:
narrow_weight = loaded_weight[..., tp_rank_start:tp_rank_end]
else:
narrow_weight = loaded_weight[..., tp_rank_start:tp_rank_end]
assert fused_name is not None
param = params_dict[fused_name]
if expert_id is None:
param.data.copy_(narrow_weight)
else:
param.data[expert_id].copy_(narrow_weight)
loaded_params.add(fused_name)
continue
elif name.endswith(".w2_weight_scale") and moe_quant_method == "fp8":
assert fused_name is not None
param = params_dict[fused_name]
if use_ep:
narrow_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
else:
narrow_weight = loaded_weight
if expert_id is None:
param.data.copy_(narrow_weight)
else:
param.data[expert_id].copy_(narrow_weight)
loaded_params.add(fused_name)
continue
# Unified handler for bias loading (w13_bias and w2_bias)
elif name.endswith(".w13_bias") or name.endswith(".w2_bias"):
is_w13_bias = name.endswith(".w13_bias")
if use_ep:
sliced_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
else:
if is_w13_bias:
if expert_id is None:
sliced_weight = loaded_weight[
:, 2 * tp_rank_start : 2 * tp_rank_end
]
else:
sliced_weight = loaded_weight[
2 * tp_rank_start : 2 * tp_rank_end
]
else:
sliced_weight = loaded_weight
if tp_rank != 0:
sliced_weight = sliced_weight.zero_()
# NOTE(rob): because gpt-oss ckpt has "unique" structure with
# fused gate_up_proj fused on disk, we cannot use the existing
# weight loaders without added complexity, so just do the
# direct load here.
assert fused_name is not None
param = params_dict[fused_name]
expert_data = param.data[expert_id]
dim1 = sliced_weight.shape[0]
expert_data.data[:dim1].copy_(sliced_weight)
loaded_params.add(fused_name)
continue
elif "sinks" in name:
# Handle attention sinks (distributed across ranks)
param = params_dict[name]
narrow_weight = loaded_weight.narrow(0, head_start, heads_per_rank)
param.data.copy_(narrow_weight)
loaded_params.add(name)
continue
for param_name, weight_name, shard_id in stacked_params_mapping:
# Skip non-stacked layers and experts (experts handled below).
if weight_name not in name:
continue
# We have mlp.experts[0].gate_proj in the checkpoint.
# Since we handle the experts below in expert_params_mapping,
# we need to skip here BEFORE we update the name, otherwise
# name will be updated to mlp.experts[0].gate_up_proj, which
# will then be updated below in expert_params_mapping
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
if ("mlp.experts." in name) and name not in params_dict:
continue
name = name.replace(weight_name, param_name)
if name.endswith("scale"):
# Remapping the name of FP8 kv-scale.
name = maybe_remap_kv_scale_name(name, params_dict)
if name is None:
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
loaded_params.add(name)
break
else:
for mapping in expert_params_mapping:
# Anyway, this is an expert weight and should not be
# attempted to load as other weights later
param_name, weight_name, mapping_expert_id, shard_id = mapping
weight_name = (
weight_name[:-1] if weight_name.endswith(".") else weight_name
)
if weight_name not in name:
continue
param = params_dict[fused_name]
# We should ask the weight loader to return success or not
# here since otherwise we may skip experts with other
# available replicas.
weight_loader = typing.cast(
Callable[..., bool], param.weight_loader
)
# Use checkpoint's expert_id for quark format (when expert_id
# is extracted from weight name), otherwise use mapping's expert_id
actual_expert_id = (
expert_id if expert_id is not None else mapping_expert_id
)
success = weight_loader(
param,
loaded_weight,
fused_name,
shard_id=shard_id,
expert_id=actual_expert_id,
return_success=True,
)
if success:
name = fused_name
loaded_params.add(name)
break
else:
if name not in params_dict:
continue
param = params_dict[name]
weight_loader = getattr(
param, "weight_loader", default_weight_loader
)
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
def _load_weights_other(
self,
ep_rank_end: int,
@@ -635,6 +1101,7 @@ class GptOssModel(nn.Module):
if hasattr(self.config, "quantization_config")
else None
)
if quant_method == "mxfp4":
return self._load_weights_mxfp4(
ep_rank_end,
@@ -644,6 +1111,15 @@ class GptOssModel(nn.Module):
weights,
stacked_params_mapping,
)
elif quant_method == "quark":
return self._load_weights_quark(
ep_rank_end,
ep_rank_start,
heads_per_rank,
head_start,
weights,
stacked_params_mapping,
)
else:
return self._load_weights_other(
ep_rank_end,
@@ -676,6 +1152,15 @@ class GptOssForCausalLM(nn.Module, SupportsPP, SupportsEagle3, SupportsLoRA):
# MoE Bias
".gate_up_proj_bias": ".w13_bias",
".down_proj_bias": ".w2_bias",
# For quark format
".gate_up_proj.weight": ".w13_weight",
".gate_up_proj.weight_scale": ".w13_weight_scale",
".gate_up_proj.bias": ".w13_bias",
".gate_up_proj.input_scale": ".w13_input_scale",
".down_proj.weight": ".w2_weight",
".down_proj.weight_scale": ".w2_weight_scale",
".down_proj.bias": ".w2_bias",
".down_proj.input_scale": ".w2_input_scale",
},
)
@@ -725,18 +1210,6 @@ class GptOssForCausalLM(nn.Module, SupportsPP, SupportsEagle3, SupportsLoRA):
logits = self.logits_processor(self.lm_head, hidden_states)
return logits
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
# Params for weights, weight scales, activation scales
# (param_name, weight_name, expert_id, shard_id)
return FusedMoE.make_expert_params_mapping(
self,
ckpt_gate_proj_name="gate_proj",
ckpt_down_proj_name="down_proj",
ckpt_up_proj_name="up_proj",
num_experts=self.config.num_local_experts,
num_redundant_experts=0,
)
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
loader = AutoWeightsLoader(
self,