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[dsv3] store expert weights such that we can transpose before grouped gemm to have column-major memory layout #1535

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[dsv3] store expert weights such that we can transpose before grouped gemm to have column-major memory layout #1535

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13 changes: 9 additions & 4 deletions torchtitan/experiments/llama4/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,11 +25,16 @@

llama4_configs = {
"debugmodel": TransformerModelArgs(
dim=256,
n_layers=6,
n_heads=16,
vocab_size=2000,
dim=5120,
n_layers=2,
n_heads=40,
n_kv_heads=8,
ffn_dim_multiplier=1.2,
multiple_of=2048,
rope_theta=500000,
max_seq_len=10485760,
num_experts=8,
interleave_moe_layer_step=1,
),
"17bx16e": TransformerModelArgs(
dim=5120,
Expand Down
32 changes: 20 additions & 12 deletions torchtitan/models/deepseek_v3/model/moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,9 +58,9 @@ def __init__(
):
super().__init__()
self.num_experts = num_experts
self.w1 = nn.Parameter(torch.empty(num_experts, dim, hidden_dim))
self.w2 = nn.Parameter(torch.empty(num_experts, hidden_dim, dim))
self.w3 = nn.Parameter(torch.empty(num_experts, dim, hidden_dim))
self.w1 = nn.Parameter(torch.empty(num_experts, hidden_dim, dim))
self.w2 = nn.Parameter(torch.empty(num_experts, dim, hidden_dim))
self.w3 = nn.Parameter(torch.empty(num_experts, hidden_dim, dim))
self.use_grouped_mm = use_grouped_mm

def forward(
Expand Down Expand Up @@ -104,9 +104,9 @@ def _run_experts_for_loop(
)
out_experts_splits = []
for expert_idx, x_expert in enumerate(x):
h = F.silu(torch.matmul(x_expert, w1[expert_idx]))
h = h * torch.matmul(x_expert, w3[expert_idx])
h = torch.matmul(h, w2[expert_idx])
h = F.silu(torch.matmul(x_expert, w1[expert_idx].transpose(-2, -1)))
h = h * torch.matmul(x_expert, w3[expert_idx].transpose(-2, -1))
h = torch.matmul(h, w2[expert_idx].transpose(-2, -1))
# h shape (tokens_per_expert(varying), dim)
out_experts_splits.append(h)
out = torch.cat(out_experts_splits, dim=0)
Expand All @@ -115,10 +115,10 @@ def _run_experts_for_loop(
out = torch.vstack((out, out.new_zeros((num_padding, out.shape[-1]))))
else:
# x shape (num_experts, tokens_per_expert, dim)
h = F.silu(torch.bmm(x, w1))
h = h * torch.bmm(x, w3)
h = F.silu(torch.bmm(x, w1.transpose(-2, -1)))
h = h * torch.bmm(x, w3.transpose(-2, -1))
# out shape (num_experts, tokens_per_expert, dim)
out = torch.bmm(h, w2)
out = torch.bmm(h, w2.transpose(-2, -1))

return out

Expand All @@ -140,9 +140,17 @@ def _run_experts_grouped_mm(
# fall back to regular bmm between 3D tensors
assert x.dim() == 3

h = F.silu(torch._grouped_mm(x.bfloat16(), w1.bfloat16(), offs=offsets))
h = h * torch._grouped_mm(x.bfloat16(), w3.bfloat16(), offs=offsets)
out = torch._grouped_mm(h, w2.bfloat16(), offs=offsets).type_as(x)
h = F.silu(
torch._grouped_mm(
x.bfloat16(), w1.bfloat16().transpose(-2, -1), offs=offsets
)
)
h = h * torch._grouped_mm(
x.bfloat16(), w3.bfloat16().transpose(-2, -1), offs=offsets
)
out = torch._grouped_mm(
h, w2.bfloat16().transpose(-2, -1), offs=offsets
).type_as(x)

return out

Expand Down
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