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183 | import torch
from torch import nn
from torch.nn import functional as F
from bench_utils import to_dtype, tensor_stats, set_seed, bench_context
from config import (
NUM_EXPERTS, HIDDEN_SIZE, TOP_K,
BATCH_SIZE, SEQ_LEN, DTYPE, DEVICE,
WEIGHT_SEED, EXPERT_SEED, INPUT_SEED, GENERAL_SEED
)
from pathlib import Path
import os
# Discover the upstream artifact directory from env
data_dir = os.environ.get('UVNOTE_INPUT_SAVE_DATA', '.')
router_weight = torch.load(Path(data_dir) / 'router_weight.pt')
router_bias = torch.load(Path(data_dir) / 'router_bias.pt')
gate_up_proj = torch.load(Path(data_dir) / 'gate_up_proj.pt')
gate_up_proj_bias = torch.load(Path(data_dir) / 'gate_up_proj_bias.pt')
down_proj = torch.load(Path(data_dir) / 'down_proj.pt')
down_proj_bias = torch.load(Path(data_dir) / 'down_proj_bias.pt')
print("Loaded shared weights from artifacts")
print(f"Router weight sum: {router_weight.sum().item():.6f}")
print(f"Gate/up sum: {gate_up_proj.sum().item():.6f}")
print(f"Down sum: {down_proj.sum().item():.6f}")
def binned_gather(x, indices, bins, expert_capacity, top_k):
E, H = bins.shape[0], x.shape[1]
out = torch.zeros((E, expert_capacity, H), device=x.device, dtype=x.dtype)
for e in range(E):
start = 0 if e == 0 else bins[e - 1]
end = bins[e]
n = min(end - start, expert_capacity)
for i in range(n):
flat_pos = indices[start + i]
tok = flat_pos // top_k
out[e, i] = x[tok]
return out
def binned_scatter(x, indices, weights, bins, expert_capacity, top_k):
E, C, H = x.shape
N = indices.shape[0] // top_k
out = torch.zeros((N, top_k, H), dtype=x.dtype, device=x.device)
for e in range(E):
start = 0 if e == 0 else bins[e - 1]
end = bins[e]
n = end - start
if n == 0:
continue
take = min(n, expert_capacity)
for i in range(take):
flat_pos = indices[start + i]
tok = flat_pos // top_k
slot = flat_pos % top_k
scale = weights[flat_pos] if weights is not None else 1.0
out[tok, slot] = x[e, i] * scale
return out.sum(dim=1)
def sort_tokens_by_expert(router_indices, num_experts):
flat_indices = router_indices.flatten()
sorted_values, sorted_indices = torch.sort(flat_indices)
tokens_per_expert = torch.bincount(sorted_values, minlength=num_experts)
bins = torch.cumsum(tokens_per_expert, dim=0)
return sorted_indices, sorted_values, bins, tokens_per_expert
def binned_experts_ref(
hidden_states,
router_indices,
routing_weights,
gate_up_proj,
gate_up_proj_bias,
down_proj,
down_proj_bias,
expert_capacity,
):
B, S, H = hidden_states.shape
E, K = routing_weights.shape[1], router_indices.shape[1]
indices, _, bins, _ = sort_tokens_by_expert(router_indices, E)
x = binned_gather(hidden_states.view(-1, H), indices, bins, expert_capacity, K)
gate_up = torch.bmm(x, gate_up_proj)
gate_up += gate_up_proj_bias[..., None, :]
gate, up = gate_up[..., ::2], gate_up[..., 1::2]
# clamp to limit
limit = 7.0
gate = gate.clamp(min=None, max=limit)
up = up.clamp(min=-limit, max=limit)
glu = gate * torch.sigmoid(gate * 1.702)
x = (up + 1) * glu
x = torch.bmm(x, down_proj) + down_proj_bias[..., None, :]
# build routing weights aligned to (token, slot)
flat_dense = routing_weights.view(-1, E)
flat_router = router_indices.view(-1, K)
selected = torch.gather(flat_dense, 1, flat_router).reshape(-1)
# scatter back
y = binned_scatter(x, indices, selected, bins, expert_capacity, K)
return y.view(B, S, H)
class BinnedRouter(nn.Module):
def __init__(self, router_weight, router_bias):
super().__init__()
self.top_k = TOP_K
self.num_experts = NUM_EXPERTS
self.hidden_dim = HIDDEN_SIZE
self.weight = nn.Parameter(router_weight.clone())
self.bias = nn.Parameter(router_bias.clone())
def forward(self, hidden_states):
hidden_states = hidden_states.reshape(-1, self.hidden_dim)
router_logits = F.linear(hidden_states, self.weight, self.bias)
router_top_value, router_indices = torch.topk(router_logits, self.top_k, dim=-1)
router_top_value = torch.nn.functional.softmax(router_top_value, dim=1, dtype=router_top_value.dtype)
router_scores = torch.zeros_like(router_logits).scatter_(1, router_indices, router_top_value)
return router_scores, router_indices
class BinnedMoEMLP(nn.Module):
def __init__(self, router_weight, router_bias, gate_up_proj, gate_up_proj_bias, down_proj, down_proj_bias):
super().__init__()
self.router = BinnedRouter(router_weight, router_bias)
self.num_experts = NUM_EXPERTS
self.hidden_size = HIDDEN_SIZE
self.expert_capacity = 256
# Expert weights - use the loaded weights
self.gate_up_proj = nn.Parameter(gate_up_proj.clone())
self.gate_up_proj_bias = nn.Parameter(gate_up_proj_bias.clone())
self.down_proj = nn.Parameter(down_proj.clone())
self.down_proj_bias = nn.Parameter(down_proj_bias.clone())
def forward(self, hidden_states):
router_scores, router_indices = self.router(hidden_states)
output = binned_experts_ref(
hidden_states,
router_indices,
router_scores,
self.gate_up_proj,
self.gate_up_proj_bias,
self.down_proj,
self.down_proj_bias,
self.expert_capacity,
)
return output, router_scores
# Run the model
set_seed(GENERAL_SEED)
device = torch.device(DEVICE)
dtype = to_dtype(DTYPE)
print("\n=== Binned Implementation ===")
# Initialize model with loaded weights
model = BinnedMoEMLP(
router_weight.to(device),
router_bias.to(device),
gate_up_proj.to(device),
gate_up_proj_bias.to(device),
down_proj.to(device),
down_proj_bias.to(device)
).to(device=device)
print(f"Router weight sum: {model.router.weight.sum().item():.6f}")
print(f"Gate/up proj sum: {model.gate_up_proj.sum().item():.6f}")
print(f"Down proj sum: {model.down_proj.sum().item():.6f}")
# Generate the same input as Yamoe
set_seed(INPUT_SEED)
x = torch.randn(BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE, device=device, dtype=dtype) * 0.1
# Benchmark the model
tokens = BATCH_SIZE * SEQ_LEN
with bench_context(warmup=10, iters=50, device=device, dtype=dtype, tokens=tokens, save_json="binned_results.json") as bench:
output, stats = bench(model, x)
print(f"\nOutput sum: {output[0].sum().item():.6f}")
|
