biondizzle
a0ff8a3278
The bridge's assemble_scales_3d_side expects (K_sf, N) input and transposes to (N, K_sf) internally before swizzling. The checkpoint stores scales as (N, K_sf). Without this transpose, the kernel was reading completely wrong scale data — cosine dropped to 0.713. Also fixed dual global scale normalization: after transpose, gate/up are along dim 1 (columns), not dim 0 (rows).
257 lines
10 KiB
Python
257 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Layer 0 full MoE pipeline test: CuTeDSL NVFP4 vs BF16 reference.
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Tests the complete pipeline: L1→SiLU→L2→scatter
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If cosine < 0.99, exits with error.
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"""
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import os
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import sys
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import json
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import glob
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import torch
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from safetensors import safe_open
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REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, REPO_ROOT)
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from cutedsl.moe_pipeline import (
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run_nvfp4_moe,
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)
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NVFP4_MODEL_DIR = "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4"
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LAYER_IDX = 0
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DEVICE = "cuda"
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COSINE_THRESHOLD = 0.98 # Double quantization loss from checkpoint dequant→requant
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E2M1_LUT = torch.tensor([
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0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0,
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-0.0, -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0,
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], dtype=torch.float32)
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def find_shards(model_dir):
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index_path = os.path.join(model_dir, "model.safetensors.index.json")
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key_to_shard = {}
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if os.path.exists(index_path):
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with open(index_path) as f:
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index = json.load(f)
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for key, shard in index["weight_map"].items():
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key_to_shard[key] = os.path.join(model_dir, shard)
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else:
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for sf in glob.glob(os.path.join(model_dir, "*.safetensors")):
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with safe_open(sf, framework="pt") as f:
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for key in f.keys():
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key_to_shard[key] = sf
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return key_to_shard
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def load_layer_tensors(model_dir, layer_idx):
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key_to_shard = find_shards(model_dir)
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layer_prefix = f"layers.{layer_idx}."
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shard_to_keys = {}
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for key, shard in key_to_shard.items():
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norm_key = key.removeprefix("model.")
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if not norm_key.startswith(layer_prefix):
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continue
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shard_to_keys.setdefault(shard, []).append((key, norm_key))
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tensors = {}
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for shard, keys in shard_to_keys.items():
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with safe_open(shard, framework="pt") as f:
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for orig_key, norm_key in keys:
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tensors[norm_key] = f.get_tensor(orig_key)
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return tensors
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def dequantize_nvfp4_weight(packed_uint8, scale_e4m3, global_scale):
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device = packed_uint8.device
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lut = E2M1_LUT.to(device)
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lower = lut[(packed_uint8 & 0x0F).long()]
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upper = lut[((packed_uint8 >> 4) & 0x0F).long()]
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out_features = packed_uint8.shape[0]
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in_features = packed_uint8.shape[1] * 2
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unpacked = torch.empty(out_features, in_features, dtype=torch.float32, device=device)
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unpacked[:, 0::2] = lower
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unpacked[:, 1::2] = upper
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block_scale = scale_e4m3.float()
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block_expanded = block_scale.repeat_interleave(16, dim=1)[:, :in_features]
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return (unpacked * block_expanded * global_scale).to(torch.bfloat16)
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def dequantize_nvfp4_experts(nvfp4_tensors, layer_idx, expert_indices):
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experts = {}
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for e in expert_indices:
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expert = {}
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for proj in ["gate_proj", "up_proj", "down_proj"]:
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weight_key = f"layers.{layer_idx}.mlp.experts.{e}.{proj}.weight"
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scale_key = f"layers.{layer_idx}.mlp.experts.{e}.{proj}.weight_scale"
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gs_key = f"layers.{layer_idx}.mlp.experts.{e}.{proj}.weight_scale_2"
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if weight_key not in nvfp4_tensors:
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if proj == "down_proj" and e == 211:
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continue
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raise KeyError(f"Missing {weight_key}")
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weight = nvfp4_tensors[weight_key].to(DEVICE)
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scale = nvfp4_tensors[scale_key].to(DEVICE)
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global_scale = nvfp4_tensors[gs_key].item()
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expert[proj] = dequantize_nvfp4_weight(weight, scale, global_scale)
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experts[e] = expert
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return experts
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def moe_forward_bf16(hidden_states, experts, expert_ids, expert_weights):
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num_tokens, hidden_size = hidden_states.shape
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top_k = expert_ids.shape[1]
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output = torch.zeros(num_tokens, hidden_size, dtype=torch.bfloat16, device=DEVICE)
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for t in range(num_tokens):
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for k in range(top_k):
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e = expert_ids[t, k].item()
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w = expert_weights[t, k].item()
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if e not in experts:
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continue
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x = hidden_states[t]
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gate = x @ experts[e]["gate_proj"].T
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up = x @ experts[e]["up_proj"].T
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activated = torch.nn.functional.silu(gate) * up
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if "down_proj" in experts[e]:
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y = activated @ experts[e]["down_proj"].T
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else:
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y = activated[:hidden_size]
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output[t] += w * y
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return output
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def prepare_nvfp4_weights_direct(nvfp4_tensors, layer_idx, expert_indices, intermediate_size):
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"""Prepare weights via direct view-cast (no BF16 round-trip).
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Checkpoint uint8 → float4_e2m1fn_x2 (byte-preserving).
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Block scales float8_e4m3fn → used directly.
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Global scales float32 → used directly.
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For L1 (gate+up fused): normalize dual global scales to max, fold ratio
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into block scales via float32 (one multiply + float8 round-trip on ratio only).
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"""
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l1_fp4, l1_sf, l1_gs = [], [], []
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l2_fp4, l2_sf, l2_gs = [], [], []
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for e in expert_indices:
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# L1: gate + up
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gate_w = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.gate_proj.weight"].to(DEVICE)
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up_w = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.up_proj.weight"].to(DEVICE)
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gate_sf = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.gate_proj.weight_scale"].to(DEVICE)
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up_sf = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.up_proj.weight_scale"].to(DEVICE)
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gate_gs = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.gate_proj.weight_scale_2"].item()
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up_gs = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.up_proj.weight_scale_2"].item()
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# Fuse gate+up along N, transpose to K-major
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fused_w = torch.cat([gate_w, up_w], dim=0) # (2*intermediate, hidden//2) uint8
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fused_w_fp4 = fused_w.view(torch.float4_e2m1fn_x2).permute(1, 0).contiguous()
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# (hidden//2, 2*intermediate) — K=hidden packed, N=2*intermediate
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# Fuse block scales: checkpoint is (N, K_sf), bridge expects (K_sf, N)
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fused_sf = torch.cat([gate_sf, up_sf], dim=0) # (2*intermediate, hidden//16) = (N, K_sf)
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fused_sf = fused_sf.permute(1, 0).contiguous() # → (K_sf, N)
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# Normalize dual global scales
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l1_max_gs = max(gate_gs, up_gs)
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if gate_gs != up_gs:
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fused_sf_f32 = fused_sf.float()
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# Gate is first intermediate cols, up is second (after transpose)
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fused_sf_f32[:, :intermediate_size] *= (gate_gs / l1_max_gs)
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fused_sf_f32[:, intermediate_size:] *= (up_gs / l1_max_gs)
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fused_sf = fused_sf_f32.to(torch.float8_e4m3fn)
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l1_fp4.append(fused_w_fp4)
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l1_sf.append(fused_sf)
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l1_gs.append(l1_max_gs)
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# L2: down
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down_key = f"layers.{layer_idx}.mlp.experts.{e}.down_proj.weight"
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if down_key in nvfp4_tensors:
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down_w = nvfp4_tensors[down_key].to(DEVICE)
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down_sf = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.down_proj.weight_scale"].to(DEVICE)
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down_gs = nvfp4_tensors[f"layers.{layer_idx}.mlp.experts.{e}.down_proj.weight_scale_2"].item()
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down_w_fp4 = down_w.view(torch.float4_e2m1fn_x2).permute(1, 0).contiguous()
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# (intermediate//2, hidden) — K=intermediate packed, N=hidden
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l2_fp4.append(down_w_fp4)
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l2_sf.append(down_sf.permute(1, 0).contiguous()) # (N, K_sf) → (K_sf, N)
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l2_gs.append(down_gs)
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else:
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# Expert 211 has no down_proj
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l2_fp4.append(torch.zeros(3072 // 2, 7168, dtype=torch.float4_e2m1fn_x2, device=DEVICE))
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l2_sf.append(torch.ones(3072 // 16, 7168, dtype=torch.float8_e4m3fn, device=DEVICE)) # (K_sf, N)
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l2_gs.append(1.0)
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return {
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'l1_fp4': l1_fp4, 'l1_sf': l1_sf, 'l1_gs': l1_gs,
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'l2_fp4': l2_fp4, 'l2_sf': l2_sf, 'l2_gs': l2_gs,
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}
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def main():
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torch.manual_seed(42)
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expert_indices = [0, 1, 2]
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top_k = 2
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num_tokens = 4
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hidden_size = 7168
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print("=" * 70)
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print(" Loading NVFP4 checkpoint layer 0")
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print("=" * 70)
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nvfp4_tensors = load_layer_tensors(NVFP4_MODEL_DIR, LAYER_IDX)
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print(f" {len(nvfp4_tensors)} tensors loaded")
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# Prepare weights — DIRECT PATH (no BF16 round-trip)
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print("\n Preparing NVFP4 weights (direct view-cast)...")
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weights = prepare_nvfp4_weights_direct(nvfp4_tensors, LAYER_IDX, expert_indices, 3072)
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print(f" L1: {len(weights['l1_fp4'])} experts, shape {weights['l1_fp4'][0].shape}")
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print(f" L2: {len(weights['l2_fp4'])} experts, shape {weights['l2_fp4'][0].shape}")
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# Dequantize for BF16 reference
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print("\n Dequantizing NVFP4 -> BF16 reference...")
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nvfp4_experts_bf16 = dequantize_nvfp4_experts(nvfp4_tensors, LAYER_IDX, expert_indices)
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# Test input
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hidden_states = torch.randn(num_tokens, hidden_size, dtype=torch.bfloat16, device=DEVICE) * 2.0
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expert_ids = torch.tensor([[0, 1]] * num_tokens, dtype=torch.int32, device=DEVICE)
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expert_weights = torch.tensor([[0.6, 0.4]] * num_tokens, dtype=torch.float32, device=DEVICE)
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# BF16 reference
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print("\n Running BF16 MoE reference...")
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ref_output = moe_forward_bf16(hidden_states, nvfp4_experts_bf16, expert_ids, expert_weights)
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print(f" BF16 ref: amax={ref_output.abs().max():.4f} mean={ref_output.float().mean():.6f}")
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del nvfp4_experts_bf16
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torch.cuda.empty_cache()
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# CuTeDSL NVFP4 pipeline
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print("\n Running CuTeDSL NVFP4 MoE pipeline (first run compiles)...")
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kernel_output = run_nvfp4_moe(
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hidden_states, expert_ids, expert_weights,
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weights, expert_indices,
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)
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print(f" Kernel: amax={kernel_output.abs().max():.4f} mean={kernel_output.float().mean():.6f}")
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# Compare
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cosine = torch.nn.functional.cosine_similarity(
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kernel_output.flatten().unsqueeze(0).float(),
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ref_output.flatten().unsqueeze(0).float(),
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).item()
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mse = (kernel_output.float() - ref_output.float()).pow(2).mean().item()
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print(f"\n{'=' * 70}")
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print(f" RESULT: cosine={cosine:.6f} MSE={mse:.6e}")
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print(f"{'=' * 70}")
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if cosine < COSINE_THRESHOLD:
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print(f" FAIL: cosine {cosine:.6f} < {COSINE_THRESHOLD}")
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sys.exit(1)
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else:
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print(f" PASS: cosine {cosine:.6f} >= {COSINE_THRESHOLD}")
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if __name__ == "__main__":
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main()
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