Update MEMORY.md and memory files with vLLM NVFP4 serving progress

Server running on B200 port 8000 with full NVFP4→vLLM bridge.
All critical bugs fixed: DeepGEMM scale format, compressor shapes, block scale values.

tmp/fix_replace_handler.py

@@ -0,0 +1,94 @@
+#!/usr/bin/python3
+"""Replace the old bf16->NVFP4 handler with a simple bf16->FP8 handler."""
+
+filepath = "/root/nvidia-meeting/deepseek-v4-quant/patches/deepseek_v4.py"
+
+with open(filepath, 'r') as f:
+    lines = f.readlines()
+
+# Find the handler: starts with the if check, ends with continue
+start = None
+end = None
+for i, line in enumerate(lines):
+    if 'loaded_weight.dtype != torch.uint8' in line and 'param.data.dtype == torch.uint8' in line:
+        # Go back to find the if statement start
+        for j in range(i, max(i-3, 0), -1):
+            if lines[j].strip().startswith('if (name.endswith'):
+                start = j
+                break
+        if start is None:
+            start = i  # fallback
+        break
+
+if start is None:
+    print("Could not find handler start")
+    exit(1)
+
+# Find the end: the first 'continue' at indent level 20+ after start
+for i in range(start + 1, min(start + 200, len(lines))):
+    stripped = lines[i].strip()
+    if stripped == 'continue':
+        indent = len(lines[i]) - len(lines[i].lstrip())
+        if indent >= 20:
+            end = i
+            break
+
+if end is None:
+    print("Could not find handler end")
+    exit(1)
+
+print(f"Replacing lines {start+1} to {end+1} ({end-start+1} lines)")
+print(f"First: {lines[start].rstrip()[:80]}")
+print(f"Last: {lines[end].rstrip()[:80]}")
+
+new_handler = [
+    '                    if (name.endswith(".weight")\n',
+    '                            and loaded_weight.dtype != torch.uint8\n',
+    '                            and param.data.dtype == torch.uint8):\n',
+    '                        # o_a_proj was NOT quantized by modelopt (bf16, no scales)\n',
+    '                        # wo_a is used with fp8_einsum: convert bf16 -> FP8 directly\n',
+    '                        w_bf16 = loaded_weight\n',
+    '                        w_amax = w_bf16.abs().amax()\n',
+    '                        if w_amax == 0:\n',
+    '                            w_amax = torch.tensor(1.0, device=w_bf16.device)\n',
+    '                        fp8_max = torch.finfo(torch.float8_e4m3fn).max\n',
+    '                        fp8_scale = w_amax / fp8_max\n',
+    '                        w_fp8 = (w_bf16 / fp8_scale).to(torch.float8_e4m3fn)\n',
+    '                        weight_scale_inv = fp8_scale.to(torch.float32)\n',
+    '                        parts = name.rsplit(".", 1)\n',
+    '                        module_path = parts[0]\n',
+    '                        mod = self\n',
+    '                        for attr in module_path.split("."):\n',
+    '                            if attr.isdigit():\n',
+    '                                mod = mod[int(attr)]\n',
+    '                            else:\n',
+    '                                mod = getattr(mod, attr)\n',
+    '                        mod.weight = torch.nn.Parameter(w_fp8, requires_grad=False)\n',
+    '                        mod.weight_scale_inv = torch.nn.Parameter(\n',
+    '                            weight_scale_inv.reshape(1), requires_grad=False\n',
+    '                        )\n',
+    '                        from vllm.model_executor.layers.linear import (\n',
+    '                            UnquantizedLinearMethod,\n',
+    '                        )\n',
+    '                        mod.quant_method = UnquantizedLinearMethod()\n',
+    '                        for attr in ("weight_scale", "weight_scale_2", "input_scale"):\n',
+    '                            if hasattr(mod, attr):\n',
+    '                                delattr(mod, attr)\n',
+    '                        loaded_params.add(name)\n',
+    '                        loaded_params.add(name.replace(".weight", ".weight_scale_inv"))\n',
+    '                        continue\n',
+]
+
+lines[start:end+1] = new_handler
+
+with open(filepath, 'w') as f:
+    f.writelines(lines)
+
+import ast
+try:
+    ast.parse(''.join(lines))
+    print("Syntax OK")
+except SyntaxError as e:
+    print(f"Syntax error at line {e.lineno}: {e.msg}")
+
+print(f"Replaced {end-start+1} lines with {len(new_handler)} lines")