biondizzle
f8533197f2
Nibble index 0 vs 8 ratio = 0.996 (FP4 -0.0 ≈ +0.0), NOT INT4 where -8 would be rare. FP4 dequant uses E2M1 LUT lookup × E8M0 scale (MXFP4 microscaling). Also adds model_opt_nvfp4_full.py for full model NVFP4 quantization.
272 lines
11 KiB
Python
272 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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Complete dequantization of DeepSeek V4 Pro mixed-precision to pure BF16.
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Handles ALL compressed tensor types found in the mixed-precision model:
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1. FP8 attention weights (float8_e4m3fn + float8_e8m0fnu block scales)
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- weight × scale_expanded → BF16
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- 128×128 block quantization
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2. FP4 (E2M1) expert weights (int8 packed + float8_e8m0fnu block scales)
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- Unpack 2 FP4 values per int8 byte (lower nibble first, upper second)
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- Dequantize via E2M1 LUT lookup × scale_expanded → BF16
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- Per-row, 32-column block scaling (MXFP4 microscaling format)
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- Output dimensions are 2× the stored dimensions
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- Verified: nibble index 0 vs 8 ratio = 0.996 (FP4 -0.0 vs +0.0),
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NOT INT4 where index 8 = -8 would be rare
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3. FP8 shared expert weights (float8_e4m3fn + float8_e8m0fnu block scales)
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- Same as FP8 attention dequantization
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After dequantization, all weights are pure BF16. FP8Linear.forward() sees
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element_size() > 1 and falls back to F.linear(), avoiding broken FP8 kernels
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on Blackwell GPUs. The model can then be loaded by modelopt without shape
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mismatches.
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"""
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import os, glob, json, shutil, sys, time
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from safetensors import safe_open
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from safetensors.torch import save_file
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import torch
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FP8_WEIGHT_DTYPE = torch.float8_e4m3fn
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FP8_SCALE_DTYPE = torch.float8_e8m0fnu
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BLOCK_SIZE_FP8 = (128, 128)
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FP4_BLOCK_SIZE = 32 # columns per scale value for MXFP4 expert weights
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# E2M1 FP4 lookup table (MXFP4 microscaling format)
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# Index 0-7: positive values (sign=0, 2-bit exp, 1-bit mantissa)
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# Index 8-15: negative values (sign=1)
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# Mapping: 0→0, 1→0.5, 2→1, 3→1.5, 4→2, 5→3, 6→4, 7→6
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FP4_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 dequantize_fp8_weight(fp8_weight: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
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"""Dequantize block-wise FP8 weight to BF16.
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fp8_weight: (out_features, in_features) float8_e4m3fn
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scale: (out_features//128, in_features//128) float8_e8m0fnu
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"""
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scale_f32 = scale.float()
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out_features, in_features = fp8_weight.shape
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scale_expanded = scale_f32.repeat_interleave(BLOCK_SIZE_FP8[0], dim=0).repeat_interleave(BLOCK_SIZE_FP8[1], dim=1)
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scale_expanded = scale_expanded[:out_features, :in_features]
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weight_bf16 = fp8_weight.float() * scale_expanded
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return weight_bf16.to(torch.bfloat16)
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def dequantize_fp4_weight(int8_packed: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
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"""Dequantize MXFP4 (E2M1) expert weight to BF16.
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FP4 values are packed 2-per-byte into int8 tensors.
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Lower nibble (bits 0-3) is the first value, upper nibble (bits 4-7) is the second.
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E2M1 format: 1 sign + 2 exponent + 1 mantissa bit.
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Scale is per-row with 32-column blocks (float8_e8m0fnu, MX microscaling).
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Output dimensions are 2× the stored dimensions.
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int8_packed: (out_features, in_features//2) int8
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scale: (out_features, in_features//32) float8_e8m0fnu
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returns: (out_features, in_features) bfloat16
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"""
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lut = FP4_E2M1_LUT.to(int8_packed.device)
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# Unpack nibble indices
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lower_idx = (int8_packed & 0x0F).long() # 0-15
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upper_idx = ((int8_packed >> 4) & 0x0F).long() # 0-15
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# LUT lookup
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lower = lut[lower_idx] # float32
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upper = lut[upper_idx] # float32
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out_features = int8_packed.shape[0]
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in_features_full = int8_packed.shape[1] * 2 # 2× expansion
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# Expand scale: (out_features, in_features//32) → (out_features, in_features)
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scale_f32 = scale.float()
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scale_expanded = scale_f32.repeat_interleave(FP4_BLOCK_SIZE, dim=1)
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scale_expanded = scale_expanded[:, :in_features_full]
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# Interleave lower and upper nibbles
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unpacked = torch.empty(out_features, in_features_full, dtype=torch.float32, device=int8_packed.device)
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unpacked[:, 0::2] = lower
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unpacked[:, 1::2] = upper
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# Dequantize: FP4 value × E8M0 scale
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bf16_weight = (unpacked * scale_expanded).to(torch.bfloat16)
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return bf16_weight
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def dequantize_model(model_dir: str, out_dir: str):
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os.makedirs(out_dir, exist_ok=True)
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# Copy non-safetensor files
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print("Copying metadata files...")
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for f in os.listdir(model_dir):
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fp = os.path.join(model_dir, f)
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if not f.endswith(".safetensors") and os.path.isfile(fp):
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shutil.copy2(fp, os.path.join(out_dir, f))
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print(f" Copied {f}")
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safetensor_files = sorted(glob.glob(os.path.join(model_dir, "*.safetensors")))
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total_shards = len(safetensor_files)
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print(f"Found {total_shards} shards")
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# First pass: build scale-key → weight-key mapping
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print("\nScanning for weight+scale pairs...")
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scale_to_weight = {}
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for f in safetensor_files:
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with safe_open(f, framework="pt") as sf:
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for key in sf.keys():
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if key.endswith(".scale"):
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weight_key = key[:-len(".scale")] + ".weight"
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scale_to_weight[key] = weight_key
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weight_to_scale = {v: k for k, v in scale_to_weight.items()}
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print(f"Found {len(scale_to_weight)} weight+scale pairs")
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# Classify weights by type (sample first 2 shards)
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fp4_weight_keys = set()
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fp8_weight_keys = set()
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scale_keys = set(scale_to_weight.keys())
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for f in safetensor_files[:2]:
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with safe_open(f, framework="pt") as sf:
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for key in sf.keys():
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if key in weight_to_scale:
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t = sf.get_tensor(key)
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if t.dtype == torch.int8:
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fp4_weight_keys.add(key)
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elif t.dtype == FP8_WEIGHT_DTYPE:
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fp8_weight_keys.add(key)
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print(f" FP4 (E2M1) expert weights (packed): ~{len(fp4_weight_keys)} per shard")
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print(f" FP8 attention/shared-expert weights: ~{len(fp8_weight_keys)} per shard")
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# Second pass: dequantize and save
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stats = {"fp4_dequantized": 0, "fp8_dequantized": 0, "scales_removed": 0, "unchanged": 0}
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start_time = time.time()
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for i, f in enumerate(safetensor_files):
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shard_start = time.time()
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tensors = {}
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scales_in_shard = {}
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with safe_open(f, framework="pt") as sf:
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keys = list(sf.keys())
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# First: collect scales
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for key in keys:
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if key in scale_keys:
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t = sf.get_tensor(key)
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scales_in_shard[key] = t
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# Second: process weights and other tensors
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for key in keys:
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if key in scale_keys:
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continue # handled separately
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t = sf.get_tensor(key)
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if key in weight_to_scale and t.dtype == torch.int8:
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# FP4 (E2M1) packed expert weight (MXFP4 microscaling)
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scale_key = weight_to_scale[key]
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scale = scales_in_shard.get(scale_key)
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if scale is None:
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print(f" WARNING: scale {scale_key} not in same shard as {key}")
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tensors[key] = t # keep as-is
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continue
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bf16 = dequantize_fp4_weight(t, scale)
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tensors[key] = bf16
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stats["fp4_dequantized"] += 1
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del scales_in_shard[scale_key]
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stats["scales_removed"] += 1
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elif key in weight_to_scale and t.dtype == FP8_WEIGHT_DTYPE:
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# FP8 weight (attention or shared expert)
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scale_key = weight_to_scale[key]
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scale = scales_in_shard.get(scale_key)
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if scale is None:
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print(f" WARNING: scale {scale_key} not in same shard as {key}")
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tensors[key] = t
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continue
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bf16 = dequantize_fp8_weight(t, scale)
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tensors[key] = bf16
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stats["fp8_dequantized"] += 1
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del scales_in_shard[scale_key]
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stats["scales_removed"] += 1
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else:
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# Regular tensor (BF16, FP32, int64, etc.) - keep as-is
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tensors[key] = t
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stats["unchanged"] += 1
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# Remove unused scales
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for sk in scales_in_shard:
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stats["scales_removed"] += 1
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out_path = os.path.join(out_dir, os.path.basename(f))
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save_file(tensors, out_path)
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shard_time = time.time() - shard_start
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elapsed = time.time() - start_time
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rate = (i + 1) / elapsed if elapsed > 0 else 0
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eta = (total_shards - i - 1) / rate if rate > 0 else 0
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print(f"[{i+1}/{total_shards}] {os.path.basename(f)} "
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f"({stats['fp4_dequantized']} fp4, {stats['fp8_dequantized']} fp8, "
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f"{stats['scales_removed']} scales rm) "
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f"[{shard_time:.1f}s, ETA: {eta/60:.0f}min]")
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del tensors, scales_in_shard
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# Update config
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cfg_path = os.path.join(out_dir, "config.json")
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if os.path.exists(cfg_path):
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cfg = json.load(open(cfg_path))
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cfg["torch_dtype"] = "bfloat16"
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cfg["_experts_implementation"] = "eager"
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if "quantization_config" in cfg:
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del cfg["quantization_config"]
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json.dump(cfg, open(cfg_path, "w"), indent=2)
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print(f"\nUpdated config.json: torch_dtype=bfloat16, _experts_implementation=eager")
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total_time = time.time() - start_time
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print(f"\nDone in {total_time/60:.1f} minutes!")
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print(f" FP4 expert weights dequantized: {stats['fp4_dequantized']}")
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print(f" FP8 weights dequantized: {stats['fp8_dequantized']}")
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print(f" Scale tensors removed: {stats['scales_removed']}")
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print(f" Unchanged tensors: {stats['unchanged']}")
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# Verify no FP8/INT8 remaining
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print("\nVerifying...")
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remaining_compressed = 0
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for f in sorted(glob.glob(os.path.join(out_dir, "*.safetensors")))[:5]:
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with safe_open(f, framework="pt") as sf:
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for key in sf.keys():
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t = sf.get_tensor(key)
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if t.dtype in (torch.float8_e8m0fnu, torch.float8_e4m3fn, torch.int8):
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remaining_compressed += 1
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if remaining_compressed <= 5:
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print(f" REMAINING: {key} {t.dtype} {t.shape}")
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if remaining_compressed == 0:
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print(" ✅ No compressed tensors remaining — model is pure BF16!")
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else:
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print(f" ⚠️ {remaining_compressed} compressed tensors still present")
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out_size = sum(os.path.getsize(os.path.join(out_dir, f)) for f in os.listdir(out_dir) if f.endswith(".safetensors"))
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print(f"Output size: {out_size / 1e12:.2f} TB")
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description="Complete dequantization of DeepSeek V4 Pro to BF16")
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parser.add_argument("model_dir", help="Path to mixed-precision model")
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parser.add_argument("out_dir", help="Path to write dequantized BF16 model")
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args = parser.parse_args()
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dequantize_model(args.model_dir, args.out_dir)
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