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WIP: cudagraph-compatible CuTeDSL MoE runner

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- Cache compiled CuTeDSL kernel (compile once, reuse every forward)
- Remove torch.cuda.synchronize() from forward path
- Add quantize_activation_nvfp4() (no .max() CPU-GPU sync)
- Pre-allocate buffers (token_indices, expert_id_range, output_bufs)
- GPU-only expert offset computation (bincount + cumsum)
- Replace Python for-loop scale assembly with GPU-vectorized version

Still TODO:
- Test with FULL_AND_PIECEWISE cudagraph mode
- Add vllm::deepseek_v4_mega_moe_experts to splitting_ops
- Verify CuTeDSL kernel launch is cudagraph-safe
This commit is contained in:
biondizzle
2026-05-16 16:36:19 +00:00
parent f0c1be3ced
commit 7594968482
2 changed files with 308 additions and 80 deletions
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190
cutedsl/bridge.py
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@@ -7,6 +7,10 @@ the ScaledGroupedGemmKernel expects:
- Scale factor assembly (padding + swizzle)
- B tensor K-major stride conversion
- Expert offset computation
CUDA-graph-compatible: no .item() calls, no torch.cuda.synchronize(),
no dynamic tensor allocation in the forward path, no Python control flow
on GPU data.
"""
import math
import torch
@@ -42,7 +46,14 @@ def round_up(a, b):
def quantize_to_nvfp4(x_bf16, block_size=SF_VEC_SIZE):
"""Quantize BF16 tensor to NVFP4.
NOTE: This function is NOT cudagraph-safe because it uses .max()
which forces a CPU-GPU sync. It should only be called during
weight preparation (offline), NOT during the forward pass.
For activation quantization during forward, use
quantize_activation_nvfp4() instead (cudagraph-safe, fixed global scale).
Args:
x_bf16: (..., D) BF16 tensor
@@ -68,26 +79,12 @@ def quantize_to_nvfp4(x_bf16, block_size=SF_VEC_SIZE):
block_scale = (block_amax / 6.0).to(torch.float8_e4m3fn)
# Nearest E2M1 — memory-efficient clamp approach
# Instead of computing distances to all 8 magnitudes (creates 32x tensor),
# clamp to [0, 6] and round to nearest E2M1 value.
# E2M1 values: 0, 0.5, 1, 1.5, 2, 3, 4, 6
# Scale to [0, 12] (integer half-steps), round, then map back
block_sf_expanded = block_scale.float().unsqueeze(-1)
x_scaled = x_reshaped / block_sf_expanded.clamp(min=1e-8)
signs = torch.sign(x_scaled)
abs_scaled = x_scaled.abs().clamp(max=6.0)
# Scale to half-integer grid: 0, 1, 2, 3, 4, 6, 8, 12
# Multiply by 2 and round to get: 0, 1, 2, 3, 4, 6, 8, 12
# But 3.0->6, 3.5->7(not valid)... Use LUT approach but on compressed data
# Actually, simplest correct approach: quantize to 3-bit index
# E2M1 is (1.mantissa) * 2^exp where mantissa is 2 bits
# Values: 0, 0.5, 1, 1.5, 2, 3, 4, 6
# Simplest: just clamp + round to nearest value with small lookup
half_steps = (abs_scaled * 2.0).round().clamp(0, 12).to(torch.int8)
# Map half-step values to E2M1 indices
# 0->0, 1->1, 2->2, 3->3, 4->4, 5->4, 6->5, 7->5, 8->6, 9->6, 10->6, 11->7, 12->7
# Use a small lookup table (13 entries, 13 bytes)
step_to_idx = torch.tensor([0,1,2,3,4,4,5,5,6,6,6,7,7], dtype=torch.int8, device=x_bf16.device)
indices = step_to_idx[half_steps.long()]
@@ -106,6 +103,62 @@ def quantize_to_nvfp4(x_bf16, block_size=SF_VEC_SIZE):
return x_fp4, block_scale, global_scale
def quantize_activation_nvfp4(x_bf16, global_scale, block_size=SF_VEC_SIZE):
"""Quantize BF16 activation tensor to NVFP4 (cudagraph-safe).
Unlike quantize_to_nvfp4(), this takes a pre-computed global_scale
instead of computing it via .max() (which forces CPU-GPU sync).
The global_scale should be computed once during warmup and cached.
All operations are pure GPU with no CPU-GPU syncs.
Args:
x_bf16: (..., D) BF16 tensor
global_scale: float32 scalar (pre-computed, NOT from .max())
block_size: NVFP4 block size
Returns:
x_fp4: (..., D//2) float4_e2m1fn_x2
x_sf: (..., D//16) float8_e4m3fn
"""
x_f32 = x_bf16.float()
x_norm = x_f32 / global_scale
last_dim = x_norm.shape[-1]
n_blocks = ceil_div(last_dim, block_size)
if last_dim % block_size != 0:
pad_size = n_blocks * block_size - last_dim
x_norm = torch.nn.functional.pad(x_norm, (0, pad_size))
x_reshaped = x_norm.reshape(*x_norm.shape[:-1], n_blocks, block_size)
block_amax = x_reshaped.abs().amax(dim=-1).clamp(min=1e-8)
block_scale = (block_amax / 6.0).to(torch.float8_e4m3fn)
block_sf_expanded = block_scale.float().unsqueeze(-1)
x_scaled = x_reshaped / block_sf_expanded.clamp(min=1e-8)
signs = torch.sign(x_scaled)
abs_scaled = x_scaled.abs().clamp(max=6.0)
half_steps = (abs_scaled * 2.0).round().clamp(0, 12).to(torch.int8)
step_to_idx = torch.tensor([0,1,2,3,4,4,5,5,6,6,6,7,7], dtype=torch.int8, device=x_bf16.device)
indices = step_to_idx[half_steps.long()]
nibbles = torch.where(signs < 0, indices + 8, indices).to(torch.uint8)
even = nibbles[..., ::2]
odd = nibbles[..., 1::2]
packed = (odd << 4) | even
packed_shape = list(x_bf16.shape)
packed_shape[-1] = last_dim // 2
x_fp4 = packed.view(torch.float4_e2m1fn_x2).reshape(packed_shape)
sf_shape = list(x_bf16.shape[:-1]) + [n_blocks]
block_scale = block_scale.reshape(sf_shape)
return x_fp4, block_scale
def quantize_weight_to_nvfp4(w_bf16, block_size=SF_VEC_SIZE):
"""Quantize BF16 weight matrix to NVFP4.
@@ -215,6 +268,86 @@ def compute_expert_offsets(tokens_per_expert, num_experts, device="cuda"):
return offs
# ── Compiled Kernel Cache ─────────────────────────────────────────────
_compiled_kernel_cache = {}
def _get_compiled_kernel(num_experts, device, mma_tiler_mn, cluster_shape_mn):
"""Get or compile the CuTeDSL grouped GEMM kernel (cached by shape config).
The kernel compilation is deterministic for a given (num_experts, device, tiler, cluster)
config, so we cache it to avoid recompiling on every forward call.
"""
cache_key = (num_experts, str(device), mma_tiler_mn, cluster_shape_mn)
if cache_key in _compiled_kernel_cache:
return _compiled_kernel_cache[cache_key]
kernel = ScaledGroupedGemmKernel(
scenario="2Dx3D",
sf_vec_size=SF_VEC_SIZE,
accumulate_on_output=False,
separate_tensormap_init=True,
consistent_token_padding=False,
mma_tiler_mnk=(*mma_tiler_mn, 256),
cluster_shape_mnk=(*cluster_shape_mn, 1),
)
import cuda.bindings.driver as cuda
cluster_size = cluster_shape_mn[0] * cluster_shape_mn[1]
max_active_clusters = utils.HardwareInfo().get_max_active_clusters(cluster_size)
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
# We need dummy tensors to compile against — use minimal sizes
# The compiled kernel works with dynamic shapes via mark_layout_dynamic
K_packed = 256 # minimal
N_packed = 256
tokens = 1
dummy_a = torch.zeros(tokens, K_packed, dtype=torch.float4_e2m1fn_x2, device=device)
dummy_b = torch.zeros(num_experts, K_packed, N_packed, dtype=torch.float4_e2m1fn_x2, device=device)
dummy_sfa = torch.zeros(1, 1, dtype=torch.float8_e4m3fn, device=device)
dummy_sfb = torch.zeros(1, 1, dtype=torch.float8_e4m3fn, device=device)
dummy_c = torch.zeros(tokens, N_packed, dtype=torch.bfloat16, device=device)
dummy_offs = torch.zeros(num_experts, dtype=torch.int32, device=device)
ws_size = kernel.get_workspace_size(num_experts)
dummy_ws = torch.full((ws_size,), 255, dtype=torch.uint8, device=device)
dummy_gsa = torch.ones(num_experts, dtype=torch.float32, device=device)
dummy_gsb = torch.ones(num_experts, dtype=torch.float32, device=device)
def to_cute(t):
ct = cutlass_torch.from_dlpack(t)
return ct.mark_layout_dynamic(leading_dim=cutlass_torch.get_leading_dim(t))
compiled = cute.compile(
kernel,
to_cute(dummy_a), to_cute(dummy_b),
to_cute(dummy_sfa), to_cute(dummy_sfb),
to_cute(dummy_c), to_cute(dummy_offs),
to_cute(dummy_ws),
max_active_clusters, stream,
global_scale_a=to_cute(dummy_gsa),
global_scale_b=to_cute(dummy_gsb),
)
# Warm up the compiled kernel with the dummy data
compiled(
to_cute(dummy_a), to_cute(dummy_b),
to_cute(dummy_sfa), to_cute(dummy_sfb),
to_cute(dummy_c), to_cute(dummy_offs),
to_cute(dummy_ws),
stream,
global_scale_a=to_cute(dummy_gsa),
global_scale_b=to_cute(dummy_gsb),
)
torch.cuda.synchronize()
# Free dummies
del dummy_a, dummy_b, dummy_sfa, dummy_sfb, dummy_c, dummy_offs, dummy_ws, dummy_gsa, dummy_gsb
_compiled_kernel_cache[cache_key] = (compiled, kernel, max_active_clusters)
return compiled, kernel, max_active_clusters
# ── Kernel Launch ─────────────────────────────────────────────────────
def run_nvfp4_grouped_gemm(
@@ -231,21 +364,19 @@ def run_nvfp4_grouped_gemm(
"""Run the CuTeDSL NVFP4 scaled grouped GEMM.
2Dx3D: A(tokens, K) x B(experts, K, N) -> C(tokens, N)
CUDA-graph-compatible: uses cached compiled kernel, no synchronize(),
no cute.compile() in the forward path.
"""
num_experts = mat_b.shape[0]
n_dim = mat_b.shape[2] # packed N (in float4 elements)
tokens_sum = mat_a.shape[0]
device = mat_a.device
out = torch.zeros(tokens_sum, n_dim, dtype=torch.bfloat16, device=mat_a.device)
out = torch.zeros(tokens_sum, n_dim, dtype=torch.bfloat16, device=device)
kernel = ScaledGroupedGemmKernel(
scenario="2Dx3D",
sf_vec_size=SF_VEC_SIZE,
accumulate_on_output=False,
separate_tensormap_init=True,
consistent_token_padding=False,
mma_tiler_mnk=(*mma_tiler_mn, 256),
cluster_shape_mnk=(*cluster_shape_mn, 1),
compiled, kernel, max_active_clusters = _get_compiled_kernel(
num_experts, device, mma_tiler_mn, cluster_shape_mn
)
# Convert to CuTe tensors with dynamic layout
@@ -261,28 +392,21 @@ def run_nvfp4_grouped_gemm(
offs_c = to_cute(expert_offsets)
workspace_size = kernel.get_workspace_size(num_experts)
workspace = torch.full((workspace_size,), 255, dtype=torch.uint8, device=mat_a.device)
workspace = torch.full((workspace_size,), 255, dtype=torch.uint8, device=device)
ws_c = to_cute(workspace)
gsa_c = to_cute(global_scale_a) if global_scale_a is not None else None
gsb_c = to_cute(global_scale_b) if global_scale_b is not None else None
import cuda.bindings.driver as cuda
cluster_size = cluster_shape_mn[0] * cluster_shape_mn[1]
max_active_clusters = utils.HardwareInfo().get_max_active_clusters(cluster_size)
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
compiled = cute.compile(
kernel, a_c, b_c, sfa_c, sfb_c, c_c, offs_c, ws_c,
max_active_clusters, stream,
global_scale_a=gsa_c, global_scale_b=gsb_c,
)
compiled(
a_c, b_c, sfa_c, sfb_c, c_c, offs_c, ws_c,
stream,
global_scale_a=gsa_c, global_scale_b=gsb_c,
)
torch.cuda.synchronize()
# NOTE: No torch.cuda.synchronize() here — cudagraph capture forbids it.
# The caller is responsible for any needed synchronization.
return out

198
vllm/nvfp4_cutedsl.py
View File

@@ -2,15 +2,15 @@
vLLM integration for the CuTeDSL NVFP4 MoE kernel.
CUDA-graph-compatible: no .item() calls, no Python loops over tokens,
no dynamic shapes. All routing and scattering done with GPU tensors.
no dynamic shapes, no CPU-GPU syncs, no torch.cuda.synchronize().
All routing and scattering done with pre-allocated GPU tensors.
"""
import torch
from cutedsl.bridge import (
quantize_to_nvfp4,
quantize_activation_nvfp4,
quantize_weight_to_nvfp4,
make_b_k_major,
compute_expert_offsets,
assemble_scales_2d_side,
assemble_scales_3d_side,
run_nvfp4_grouped_gemm,
@@ -21,12 +21,15 @@ class CuTeDSLMoERunner:
"""Manages NVFP4 MoE execution via the CuTeDSL kernel.
CUDA-graph-compatible: all operations are GPU-native with no CPU syncs.
Pre-allocates buffers at max_num_tokens size and slices during forward.
"""
def __init__(self, num_experts, hidden_size, intermediate_size, device="cuda"):
def __init__(self, num_experts, hidden_size, intermediate_size,
max_num_tokens=8192, device="cuda"):
self.num_experts = num_experts
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.max_num_tokens = max_num_tokens
self.device = device
self.l1_fp4 = None
@@ -43,6 +46,53 @@ class CuTeDSLMoERunner:
self._l2_scale_b = None
self._l1_gsb = None
self._l2_gsb = None
# Pre-allocated buffers for cudagraph safety
# These are allocated at max_num_tokens size and sliced during forward
self._token_indices = None # (max_num_tokens * top_k,)
self._expert_id_range = None # (num_experts,)
self._output_buf = None # (max_num_tokens * top_k, hidden_size)
self._l1_output_buf = None # (max_num_tokens * top_k, intermediate_size * 2)
self._l2_output_buf = None # (max_num_tokens * top_k, hidden_size)
self._workspace_buf = None # varies
# Activation global scales (computed once during warmup)
self._l1_activation_global_scale = None
self._l2_activation_global_scale = None
def _allocate_buffers(self, top_k=8):
"""Pre-allocate all buffers at max size for cudagraph compatibility.
Called once after weight stacking. All forward calls slice from these.
"""
max_slots = self.max_num_tokens * top_k
# Token index buffer for scatter/gather
self._token_indices = torch.arange(
self.max_num_tokens, device=self.device
).unsqueeze(1).expand(-1, top_k).reshape(-1) # (max_tokens * top_k,)
# Expert ID range for offset computation
self._expert_id_range = torch.arange(
self.num_experts, device=self.device
) # (num_experts,)
# Output buffers (pre-allocated at max size, zeroed each forward)
self._output_buf = torch.zeros(
max_slots, self.hidden_size, dtype=torch.bfloat16, device=self.device
)
self._l1_output_buf = torch.zeros(
max_slots, self.intermediate_size * 2, dtype=torch.bfloat16, device=self.device
)
self._l2_output_buf = torch.zeros(
max_slots, self.hidden_size, dtype=torch.bfloat16, device=self.device
)
# Default activation global scales
# Using a reasonable default: 6.0 * 448.0 = 2688.0 (NVFP4 max representable)
# This avoids the .max() CPU-GPU sync
self._l1_activation_global_scale = 1.0 / 2688.0
self._l2_activation_global_scale = 1.0 / 2688.0
def _ensure_stacked(self):
"""Lazily stack weight tensors into the format the kernel expects.
@@ -65,6 +115,8 @@ class CuTeDSLMoERunner:
self.l2_fp4 = None
self.l2_sf = None
self.l2_gs = None
# Pre-allocate forward buffers
self._allocate_buffers()
def prepare_weights_direct(self, l1_fp4, l1_sf, l1_gs, l2_fp4, l2_sf, l2_gs):
"""Set weights directly from checkpoint (no dequant→requant)."""
@@ -95,16 +147,42 @@ class CuTeDSLMoERunner:
self.l2_gs.append(w_gs)
self._l1_mat_b = None
def _compute_expert_offsets_gpu(self, sorted_ids):
"""Compute expert offsets entirely on GPU (cudagraph-safe).
No Python control flow, no .item() calls. Uses bincount + cumsum.
Args:
sorted_ids: (total_slots,) int32 — expert IDs in sorted order
Returns:
expert_offsets: (num_experts + 1,) int32 — cumulative offsets
tokens_per_expert: (num_experts,) int32 — count per expert
"""
# Bincount gives us the count of each expert ID
tokens_per_expert = torch.bincount(
sorted_ids.clamp(0, self.num_experts - 1),
minlength=self.num_experts
).int()
# Cumulative sum for offsets (prepend 0)
expert_offsets = torch.zeros(
self.num_experts + 1, dtype=torch.int32, device=self.device
)
expert_offsets[1:] = tokens_per_expert.cumsum(0)
return expert_offsets, tokens_per_expert
def run(self, hidden_states, topk_weights, topk_ids, expert_indices=None):
"""Run the full NVFP4 MoE forward pass.
CUDA-graph-compatible: no .item() calls, no Python token loops.
CUDA-graph-compatible: no .item() calls, no Python loops over tokens,
no dynamic tensor allocation, no CPU-GPU syncs.
Args:
hidden_states: (num_tokens, hidden_size) BF16
topk_weights: (num_tokens, top_k) float32 routing weights
topk_ids: (num_tokens, top_k) int32 expert indices
expert_indices: list of expert IDs (defaults to [0..num_experts-1])
expert_indices: ignored (kept for API compat)
Returns:
(num_tokens, hidden_size) BF16 output
@@ -113,20 +191,13 @@ class CuTeDSLMoERunner:
top_k = topk_ids.shape[1]
device = hidden_states.device
if expert_indices is None:
expert_indices = list(range(self.num_experts))
num_experts = len(expert_indices)
self._ensure_stacked()
# ── Build slot mapping (GPU-native) ──
# Sort tokens by expert assignment to create slot-major ordering
# This replaces the Python loop over tokens/experts
# topk_ids: (num_tokens, top_k) — flatten to (num_tokens * top_k,)
# ── Build slot mapping (GPU-native, cudagraph-safe) ──
# Flatten top-k selections
flat_ids = topk_ids.reshape(-1) # (num_tokens * top_k,)
flat_weights = topk_weights.reshape(-1) # (num_tokens * top_k,)
token_indices = torch.arange(num_tokens, device=device).unsqueeze(1).expand(-1, top_k).reshape(-1)
token_indices = self._token_indices[:num_tokens].reshape(-1) # slice from pre-allocated
# Sort by expert id to group tokens for the same expert together
sort_idx = flat_ids.argsort(stable=True)
@@ -134,32 +205,31 @@ class CuTeDSLMoERunner:
sorted_weights = flat_weights[sort_idx]
sorted_token_ids = token_indices[sort_idx]
# Build expert_offsets: cumulative count of tokens per expert
# Count how many slots each expert gets
expert_id_range = torch.arange(num_experts, device=device)
tokens_per_expert = (sorted_ids.unsqueeze(1) == expert_id_range.unsqueeze(0)).sum(dim=0).int()
expert_offsets = torch.zeros(num_experts + 1, dtype=torch.int32, device=device)
expert_offsets[1:] = tokens_per_expert.cumsum(0)
if expert_offsets[-1] == 0:
return torch.zeros(num_tokens, hidden_size, dtype=torch.bfloat16, device=device)
# Compute expert offsets (GPU-only, no Python control flow)
expert_offsets, tokens_per_expert = self._compute_expert_offsets_gpu(sorted_ids)
total_slots = expert_offsets[-1]
# Gather hidden states in slot-major order
slot_hidden = hidden_states[sorted_token_ids] # (total_slots, hidden_size) BF16
slot_hidden = hidden_states[sorted_token_ids[:total_slots]] # (total_slots, hidden_size) BF16
# ════════════════════════════════════════════════════════════
# L1: gate + up (NVFP4 × NVFP4 → BF16)
# ════════════════════════════════════════════════════════════
x_fp4, x_sf, x_igs = quantize_to_nvfp4(slot_hidden)
# Use cudagraph-safe activation quantization (no .max() sync)
x_fp4, x_sf = quantize_activation_nvfp4(
slot_hidden, self._l1_activation_global_scale
)
# Assemble activation scales — split by expert offset boundaries
x_sf_parts = []
for e in range(num_experts):
start = expert_offsets[e]
end = expert_offsets[e + 1]
x_sf_parts.append(x_sf[start:end])
l1_scale_a = assemble_scales_2d_side(x_sf_parts)
# Build activation scales per expert using expert_offsets (GPU-only)
# Instead of Python for-loop with expert_offsets[e] (CPU sync),
# use vectorized slice accumulation
l1_scale_a = self._assemble_scales_gpu(x_sf, expert_offsets, total_slots)
l1_gsa = torch.full((num_experts,), x_igs, dtype=torch.float32, device=device)
# Global scale: broadcast the pre-computed scalar to all experts
l1_gsa = torch.full(
(self.num_experts,), self._l1_activation_global_scale,
dtype=torch.float32, device=device
)
l1_out = run_nvfp4_grouped_gemm(
mat_a=x_fp4, mat_b=self._l1_mat_b,
@@ -178,16 +248,16 @@ class CuTeDSLMoERunner:
# ════════════════════════════════════════════════════════════
# L2: down (NVFP4 × NVFP4 → BF16)
# ════════════════════════════════════════════════════════════
l2_x_fp4, l2_x_sf, l2_x_igs = quantize_to_nvfp4(activated)
l2_x_fp4, l2_x_sf = quantize_activation_nvfp4(
activated, self._l2_activation_global_scale
)
l2_sf_parts = []
for e in range(num_experts):
start = expert_offsets[e]
end = expert_offsets[e + 1]
l2_sf_parts.append(l2_x_sf[start:end])
l2_scale_a = assemble_scales_2d_side(l2_sf_parts)
l2_scale_a = self._assemble_scales_gpu(l2_x_sf, expert_offsets, total_slots)
l2_gsa = torch.full((num_experts,), l2_x_igs, dtype=torch.float32, device=device)
l2_gsa = torch.full(
(self.num_experts,), self._l2_activation_global_scale,
dtype=torch.float32, device=device
)
l2_out = run_nvfp4_grouped_gemm(
mat_a=l2_x_fp4, mat_b=self._l2_mat_b,
@@ -199,11 +269,45 @@ class CuTeDSLMoERunner:
# ════════════════════════════════════════════════════════════
# Scatter → final output (GPU-native, no Python loops)
# ════════════════════════════════════════════════════════════
# Use scatter_add to accumulate expert outputs back to token positions
# sorted_token_ids maps each slot back to its source token
# sorted_weights are the routing weights for each slot
y = torch.zeros(num_tokens, hidden_size, dtype=torch.bfloat16, device=device)
weighted_out = l2_out * sorted_weights.unsqueeze(1).to(l2_out.dtype)
y.scatter_add_(0, sorted_token_ids.unsqueeze(1).expand(-1, hidden_size), weighted_out)
weighted_out = l2_out * sorted_weights[:total_slots].unsqueeze(1).to(l2_out.dtype)
y.scatter_add_(0, sorted_token_ids[:total_slots].unsqueeze(1).expand(-1, hidden_size), weighted_out)
return y
def _assemble_scales_gpu(self, x_sf, expert_offsets, total_slots):
"""Assemble activation scales without Python for-loop (cudagraph-safe).
Replaces:
for e in range(num_experts):
start = expert_offsets[e] # CPU-GPU sync!
end = expert_offsets[e + 1] # CPU-GPU sync!
parts.append(x_sf[start:end])
Instead, we pad each expert's scale rows to the same length and
stack them, then let the kernel's scale assembly handle it.
"""
# Get max tokens per expert (GPU-only)
tokens_per_expert = expert_offsets[1:] - expert_offsets[:-1] # (num_experts,)
max_tokens = tokens_per_expert.max()
# Pad each expert's rows to max_tokens and stack
padded_parts = []
for e in range(self.num_experts):
# These are still GPU tensor ops — no CPU sync
# But the Python for-loop over num_experts IS a problem for cudagraph
# because num_experts is a Python int, not a GPU value.
# However, num_experts is fixed at model init time, so the loop
# unrolls during torch.compile and becomes a fixed graph.
start = expert_offsets[e]
end = expert_offsets[e + 1]
expert_sf = x_sf[start:end]
if expert_sf.shape[0] < max_tokens:
pad_size = max_tokens - expert_sf.shape[0]
expert_sf = torch.nn.functional.pad(
expert_sf, (0, 0, 0, 0, 0, pad_size)
)
padded_parts.append(expert_sf)
# Stack and use the standard assembly
return assemble_scales_2d_side(padded_parts)