From 9b0858aa35455669b842a5d4ce5158fd49291bb2 Mon Sep 17 00:00:00 2001
From: biondizzle <biondizzle@gmail.com>
Date: Sat, 30 May 2026 22:39:01 +0000
Subject: [PATCH] =?UTF-8?q?Add=20single=5Fshot=5Finference.py=20=E2=80=94?=
 =?UTF-8?q?=20baseline=20kernel=20verification?=
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Streams weights one layer at a time from 95 safetensors shards.
NVFP4 dequant → BF16 matmul for baseline (production uses tcgen05 MMA).
Runs token-by-token decode loop with production FMHA kernel.

Known gaps for first run:
- FFN (MoE) skipped — not the kernel under test
- mHC simplified — not the kernel under test
- RoPE skipped in baseline
- compressor/indexer bypassed (raw KV for now)

FMHA kernel is the component under test (cos ≥ 0.999993).
---
 single_shot_inference.py | 501 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 501 insertions(+)
 create mode 100644 single_shot_inference.py

diff --git a/single_shot_inference.py b/single_shot_inference.py
new file mode 100644
index 00000000..a8617d10
--- /dev/null
+++ b/single_shot_inference.py
@@ -0,0 +1,501 @@
+#!/usr/bin/env python3
+"""Single-shot DSV4 inference — baseline kernel verification.
+
+Runs one deterministic inference request through the production kernel
+stack WITHOUT vLLM/sglang. This is a bare-metal test to verify kernel
+correctness end-to-end.
+
+Usage (on B200):
+    source /root/dsv4-nvfp4-workspace/venv/bin/activate
+    cd /root/dsv4-nvfp4-workspace/kernel
+    python single_shot_inference.py
+
+Design:
+- Loads weights one layer at a time (streaming, ~15GB peak)
+- Runs decode loop: token-by-token autoregressive generation
+- Uses the production FMHA kernel via dsv4_attention
+- Verifies against expected output ("Paris" for "The capital of France is")
+- No vLLM, no sglang, no serving framework — just the kernel
+"""
+import os
+import sys
+import time
+import torch
+import json
+from pathlib import Path
+
+# ---- Paths ----
+CHECKPOINT_DIR = "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4"
+VENV = "/root/dsv4-nvfp4-workspace/venv"
+
+# ---- Config ----
+MAX_NEW_TOKENS = 10
+PROMPT = "The capital of France is"
+
+# =====================================================================
+# Weight loading — stream from safetensors shards
+# =====================================================================
+
+class CheckpointReader:
+    """Lazy reader for DSV4 safetensors shards.
+    
+    Instead of loading all 95 shards (945GB), provides per-layer access
+    by loading individual shards and extracting the relevant keys.
+    """
+    def __init__(self, checkpoint_dir: str):
+        self.dir = Path(checkpoint_dir)
+        self._index = None
+        self._shard_cache = {}  # shard_idx -> dict
+        self._weight_map = None  # key -> shard_idx
+        self._build_index()
+    
+    def _build_index(self):
+        """Build the weight→shard mapping from the model.safetensors.index.json."""
+        index_path = self.dir / "model.safetensors.index.json"
+        if index_path.exists():
+            with open(index_path) as f:
+                idx = json.load(f)
+            self._weight_map = idx.get("weight_map", {})
+        else:
+            # No index — load all shards (will be slow, but works)
+            self._weight_map = {}
+            print("WARNING: No index file found, will scan all shards")
+    
+    def _load_shard(self, shard_name: str):
+        """Load a single shard file."""
+        if shard_name in self._shard_cache:
+            return self._shard_cache[shard_name]
+        path = self.dir / shard_name
+        if not path.exists():
+            return None
+        from safetensors.torch import load_file
+        print(f"  Loading shard: {shard_name}")
+        data = load_file(str(path))
+        self._shard_cache[shard_name] = data
+        return data
+    
+    def get_weight(self, key: str):
+        """Get a single weight tensor by key."""
+        if self._weight_map and key in self._weight_map:
+            shard_name = self._weight_map[key]
+            shard = self._load_shard(shard_name)
+            if shard and key in shard:
+                return shard[key]
+        # Fallback: scan all shards
+        for i in range(1, 96):
+            shard_name = f"model-{i:05d}-of-00095.safetensors"
+            shard = self._load_shard(shard_name)
+            if shard and key in shard:
+                return shard[key]
+        return None
+    
+    def get_layer_weights(self, layer_idx: int):
+        """Get all weights for a single layer."""
+        prefix = f"model.layers.{layer_idx}."
+        weights = {}
+        
+        if self._weight_map:
+            # Find which shards contain this layer
+            shard_names = set()
+            for key, shard in self._weight_map.items():
+                if key.startswith(prefix):
+                    shard_names.add(shard)
+            
+            for shard_name in shard_names:
+                shard = self._load_shard(shard_name)
+                if shard:
+                    for key, value in shard.items():
+                        if key.startswith(prefix):
+                            weights[key] = value
+        else:
+            # Scan all shards
+            for i in range(1, 96):
+                shard_name = f"model-{i:05d}-of-00095.safetensors"
+                shard = self._load_shard(shard_name)
+                if shard:
+                    for key, value in shard.items():
+                        if key.startswith(prefix):
+                            weights[key] = value
+        
+        return weights
+    
+    def clear_cache(self):
+        """Free cached shard data."""
+        self._shard_cache.clear()
+        torch.cuda.empty_cache()
+
+
+# =====================================================================
+# Tokenizer — simple BPE via transformers
+# =====================================================================
+
+def load_tokenizer():
+    """Load the DSV4 tokenizer."""
+    from transformers import AutoTokenizer
+    return AutoTokenizer.from_pretrained(CHECKPOINT_DIR)
+
+
+# =====================================================================
+# Model config from checkpoint
+# =====================================================================
+
+def load_config():
+    """Load model config from checkpoint."""
+    with open(os.path.join(CHECKPOINT_DIR, "config.json")) as f:
+        return json.load(f)
+
+
+# =====================================================================
+# NVFP4 Linear — weight loading + forward
+# =====================================================================
+
+class NVFP4Linear:
+    """NVFP4 quantized linear layer.
+    
+    Stores weight as uint8 (2 FP4 per byte) + E4M3 per-16-element scale + global_scale.
+    Forward: dequantize → BF16 matmul (for baseline; production uses tcgen05 MMA).
+    """
+    def __init__(self, in_features: int, out_features: int):
+        self.in_features = in_features
+        self.out_features = out_features
+        self.weight = None       # (out, in/2) uint8 — packed FP4
+        self.weight_scale = None # (out, in/16) float8_e4m3fn — per-16 scale
+        self.global_scale = None # scalar float32
+        self._bias = None
+    
+    def load_from_checkpoint(self, weight: torch.Tensor, weight_scale: torch.Tensor, 
+                              global_scale: torch.Tensor = None):
+        """Load from checkpoint tensors."""
+        self.weight = weight.cuda()
+        self.weight_scale = weight_scale.cuda()
+        if global_scale is not None:
+            self.global_scale = global_scale.cuda()
+    
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Forward pass: dequantize → BF16 → matmul.
+        
+        This is the BASELINE path. Production uses tcgen05 MMA.
+        For verification, BF16 matmul after dequant is correct.
+        """
+        if self.weight is None:
+            raise RuntimeError("Weights not loaded")
+        
+        # Dequantize NVFP4 → BF16
+        # weight: (out, in/2) uint8 — 2 FP4 values per byte
+        # weight_scale: (out, in/16) float8_e4m3fn — 1 scale per 16 elements
+        w_bf16 = self._dequant_nvfp4(self.weight, self.weight_scale, self.global_scale)
+        
+        # Standard BF16 matmul
+        return torch.nn.functional.linear(x, w_bf16)
+    
+    def _dequant_nvfp4(self, weight: torch.Tensor, scale: torch.Tensor, 
+                        global_scale: torch.Tensor) -> torch.Tensor:
+        """Dequantize NVFP4 weight to BF16.
+        
+        NVFP4: each 16-element group has 1 E4M3 scale.
+        Each byte contains 2 FP4 (E2M1) values: high nibble = second, low nibble = first.
+        Dequant = FP4 * E4M3_scale * global_scale
+        """
+        out_dim = weight.shape[0]
+        in_dim_packed = weight.shape[1]  # in_features / 2
+        in_features = in_dim_packed * 2
+        group_size = 16
+        
+        # Unpack nibbles → (out, in) FP4 values
+        low_nibbles = (weight & 0x0F).to(torch.int8)   # (out, in/2)
+        high_nibbles = (weight >> 4).to(torch.int8)     # (out, in/2)
+        
+        # FP4 E2M1 values: sign(1) + exp(2) + mantissa(1)
+        # Values: ±{0, 2, 3, 4, 6, 8, 12, Inf}
+        # Simple LUT approach for correctness
+        fp4_lut = torch.tensor([0, 2, 3, 4, 6, 8, 12, float('inf')], 
+                               dtype=torch.float32, device=weight.device)
+        
+        # Handle sign bit (bit 3)
+        low_signs = (low_nibbles >> 3).bool()
+        low_vals = low_nibbles & 0x07
+        high_signs = (high_nibbles >> 3).bool()  
+        high_vals = high_nibbles & 0x07
+        
+        low_f = fp4_lut[low_vals] * torch.where(low_signs, -1.0, 1.0)
+        high_f = fp4_lut[high_vals] * torch.where(high_signs, -1.0, 1.0)
+        
+        # Interleave: [low0, high0, low1, high1, ...]
+        w_f = torch.stack([low_f, high_f], dim=-1).reshape(out_dim, in_features)
+        
+        # Apply per-16-element scales
+        # scale: (out, in/16) float8_e4m3fn
+        scale_f = scale.float()  # E4M3 → float32
+        if global_scale is not None:
+            scale_f = scale_f * global_scale.float()
+        
+        # Expand scales: (out, in/16) → (out, in)
+        n_groups = scale_f.shape[1]
+        scale_expanded = scale_f.repeat_interleave(group_size, dim=1)
+        
+        w_dequant = (w_f * scale_expanded).to(torch.bfloat16)
+        return w_dequant
+
+
+class BF16Linear:
+    """Standard BF16 linear layer (for o_a_proj, embeddings, etc)."""
+    def __init__(self, in_features: int, out_features: int):
+        self.in_features = in_features
+        self.out_features = out_features
+        self.weight = None
+    
+    def load_from_checkpoint(self, weight: torch.Tensor):
+        self.weight = weight.cuda().to(torch.bfloat16)
+    
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.nn.functional.linear(x, self.weight)
+
+
+# =====================================================================
+# Single layer forward
+# =====================================================================
+
+def forward_layer(
+    x: torch.Tensor,           # (T, hidden_size) BF16
+    layer_weights: dict,        # checkpoint weights for this layer
+    layer_idx: int,
+    config: dict,
+) -> torch.Tensor:
+    """Forward pass through one transformer layer.
+    
+    Simplified baseline: uses BF16 matmul after NVFP4 dequant.
+    This is mathematically equivalent to the tcgen05 MMA path.
+    """
+    hidden_size = config["hidden_size"]
+    num_heads = config["num_attention_heads"]  # 128 for Pro
+    head_dim = config["head_dim"]              # 512
+    rope_dim = config["rope_dim"]              # 64
+    n_hc = config.get("n_hc", 4)
+    nope_dim = head_dim - rope_dim  # 448
+    
+    # ---- mHC pre-block (simplified: identity for baseline) ----
+    # TODO: implement mHC properly with weights from attn_hc.*
+    # For baseline, just pass through
+    
+    # ---- RMSNorm ----
+    norm_weight = layer_weights.get(f"model.layers.{layer_idx}.self_attn.kv_norm.weight")
+    # Actually the norm weight key might be different
+    # Let's skip norm for now (will add once we know the exact key names)
+    
+    # ---- Attention ----
+    prefix = f"model.layers.{layer_idx}.self_attn"
+    
+    # Q projection: q_a_proj (low-rank down) → q_b_proj (low-rank up)
+    q_a_w = layer_weights.get(f"{prefix}.q_a_proj.weight")
+    q_a_s = layer_weights.get(f"{prefix}.q_a_proj.weight_scale")
+    q_b_w = layer_weights.get(f"{prefix}.q_b_proj.weight")
+    q_b_s = layer_weights.get(f"{prefix}.q_b_proj.weight_scale")
+    
+    if q_a_w is not None:
+        q_down = NVFP4Linear(hidden_size, q_a_w.shape[0])
+        q_down.load_from_checkpoint(q_a_w, q_a_s)
+        q_up = NVFP4Linear(q_a_w.shape[0] * 2, num_heads * head_dim)  # 768*2=1536 for Pro
+        q_up.load_from_checkpoint(q_b_w, q_b_s)
+        c_Q = q_down.forward(x)
+        q = q_up.forward(c_Q)
+    else:
+        raise RuntimeError(f"Missing q_a_proj weights for layer {layer_idx}")
+    
+    # KV projection
+    kv_w = layer_weights.get(f"{prefix}.kv_proj.weight")
+    kv_s = layer_weights.get(f"{prefix}.kv_proj.weight_scale")
+    if kv_w is not None:
+        kv_down = NVFP4Linear(hidden_size, kv_w.shape[0])
+        kv_down.load_from_checkpoint(kv_w, kv_s)
+        kv = kv_down.forward(x)  # (T, kv_dim) — depends on layer type
+    else:
+        raise RuntimeError(f"Missing kv_proj weights for layer {layer_idx}")
+    
+    # Reshape Q: (T, n_h * hd) → (n_h, T, hd)
+    T = q.shape[0]
+    q_heads = q.reshape(T, num_heads, head_dim).permute(1, 0, 2)  # (n_h, T, hd)
+    
+    # Apply partial RoPE (last 64 dims)
+    # For baseline, skip RoPE — the kernel handles it internally
+    # TODO: apply forward_rope_partial
+    
+    # K/V reshape: MQA (1 KV head)
+    # kv shape depends on layer type:
+    # HCA: (T, head_dim) — single stream
+    # CSA: (T, 4*head_dim) — (Ca, Cb, Za, Zb)
+    # SWA: (T, head_dim)
+    kv_dim = kv.shape[-1]
+    
+    if kv_dim == head_dim:
+        # HCA or SWA: single KV stream
+        k = kv.reshape(T, 1, head_dim).permute(1, 0, 2)  # (1, T, hd)
+        v = k.clone()
+    elif kv_dim == 4 * head_dim:
+        # CSA: split into Ca, Cb, Za, Zb
+        ca, cb, za, zb = kv.chunk(4, dim=-1)
+        # For baseline, just use Ca as K, V = K (simplified)
+        k = ca.reshape(T, 1, head_dim).permute(1, 0, 2)
+        v = k.clone()
+    elif kv_dim == 2 * head_dim:
+        # HCA: (C, Z)
+        c, z = kv.chunk(2, dim=-1)
+        k = c.reshape(T, 1, head_dim).permute(1, 0, 2)
+        v = k.clone()
+    else:
+        raise RuntimeError(f"Unexpected kv_dim={kv_dim} for layer {layer_idx}")
+    
+    # Run FMHA
+    from dsv4.kernels.attention.production import dsv4_attention
+    attn_out = dsv4_attention(q_heads, k, v)  # (n_h, T, hd)
+    
+    # Reshape back: (n_h, T, hd) → (T, n_h * hd)
+    attn_out = attn_out.permute(1, 0, 2).reshape(T, num_heads * head_dim)
+    
+    # Output projection
+    # o_a_proj: grouped BF16 (n_h * hd, n_groups * o_rank)
+    # o_b_proj: NVFP4 (n_groups * o_rank, hidden_size)
+    o_a_w = layer_weights.get(f"{prefix}.o_a_proj.weight")
+    o_b_w = layer_weights.get(f"{prefix}.o_b_proj.weight")
+    o_b_s = layer_weights.get(f"{prefix}.o_b_proj.weight_scale")
+    
+    if o_a_w is not None and o_b_w is not None:
+        # o_a is BF16 grouped linear — for baseline, treat as dense
+        o_a = BF16Linear(num_heads * head_dim, o_a_w.shape[0])
+        o_a.load_from_checkpoint(o_a_w)
+        o_b = NVFP4Linear(o_a_w.shape[0], hidden_size)
+        o_b.load_from_checkpoint(o_b_w, o_b_s)
+        attn_proj = o_a.forward(attn_out)
+        attn_out = o_b.forward(attn_proj)
+    else:
+        raise RuntimeError(f"Missing output projection weights for layer {layer_idx}")
+    
+    # ---- Residual ----
+    x = x + attn_out
+    
+    # ---- FFN (simplified: skip MoE for baseline) ----
+    # The FFN is a massive MoE with 384 experts, each ~3072×7168.
+    # For a baseline single-shot test, we can skip the FFN or use a
+    # simplified version. The FFN is not the kernel under test.
+    # TODO: implement MoE forward with NVFP4 GEMM
+    print(f"    Layer {layer_idx}: attention OK, skipping FFN for baseline")
+    
+    return x
+
+
+# =====================================================================
+# Main inference loop
+# =====================================================================
+
+def main():
+    print("=" * 70)
+    print("DSV4 Single-Shot Inference — Baseline Kernel Verification")
+    print("=" * 70)
+    
+    # ---- Load config ----
+    config = load_config()
+    num_layers = config["num_hidden_layers"]
+    hidden_size = config["hidden_size"]
+    num_heads = config["num_attention_heads"]
+    head_dim = config["head_dim"]
+    print(f"\nModel: {config.get('model_type', 'deepseek_v4')}")
+    print(f"Layers: {num_layers}, Heads: {num_heads}, Head dim: {head_dim}")
+    print(f"Hidden: {hidden_size}")
+    
+    # ---- Load tokenizer ----
+    print("\nLoading tokenizer...")
+    tokenizer = load_tokenizer()
+    input_ids = tokenizer.encode(PROMPT, return_tensors="pt").cuda()
+    print(f"Prompt: '{PROMPT}'")
+    print(f"Token IDs: {input_ids.tolist()}")
+    
+    # ---- Load checkpoint reader ----
+    print("\nInitializing checkpoint reader...")
+    reader = CheckpointReader(CHECKPOINT_DIR)
+    
+    # ---- Load embedding + final norm + lm_head ----
+    print("\nLoading embedding layer...")
+    embed_weight = reader.get_weight("model.embed_tokens.weight")
+    if embed_weight is not None:
+        embed = torch.nn.Embedding.from_pretrained(embed_weight.cuda().to(torch.bfloat16))
+    else:
+        raise RuntimeError("Missing embedding weights")
+    
+    print("Loading final norm + lm_head...")
+    norm_weight = reader.get_weight("model.norm.weight")
+    if norm_weight is None:
+        # Try alternate key
+        norm_weight = reader.get_weight("model.model.norm.weight")
+    
+    lm_head_weight = reader.get_weight("lm_head.weight")
+    if lm_head_weight is None:
+        # Often tied with embedding
+        lm_head_weight = embed_weight
+        print("  lm_head tied with embedding")
+    lm_head = BF16Linear(hidden_size, config["vocab_size"])
+    lm_head.load_from_checkpoint(lm_head_weight.cuda().to(torch.bfloat16))
+    
+    # ---- Decode loop ----
+    print(f"\nStarting decode loop (max {MAX_NEW_TOKENS} tokens)...")
+    generated_ids = input_ids[0].tolist()
+    
+    for step in range(MAX_NEW_TOKENS):
+        t0 = time.time()
+        current_pos = len(generated_ids) - 1
+        token_id = torch.tensor([generated_ids[-1]], dtype=torch.long, device='cuda')
+        
+        # Embed
+        x = embed(token_id).unsqueeze(0)  # (1, 1, hidden_size) → (1, hidden_size)
+        x = x.squeeze(0)  # (1, hidden_size) for T=1 decode
+        
+        # Process through layers
+        for layer_idx in range(num_layers):
+            layer_weights = reader.get_layer_weights(layer_idx)
+            if not layer_weights:
+                print(f"  WARNING: No weights for layer {layer_idx}, skipping")
+                continue
+            
+            x = forward_layer(x, layer_weights, layer_idx, config)
+            
+            # Free layer weights after use
+            del layer_weights
+            if layer_idx % 10 == 9:
+                reader.clear_cache()
+                torch.cuda.empty_cache()
+        
+        # Final norm + lm_head
+        if norm_weight is not None:
+            x_f = x.float()
+            rms = x_f.pow(2).mean(dim=-1, keepdim=True).add(1e-6).rsqrt()
+            x = (x_f * rms * norm_weight.cuda().float()).to(torch.bfloat16)
+        
+        logits = lm_head.forward(x)  # (1, vocab_size)
+        next_token = torch.argmax(logits, dim=-1).item()
+        generated_ids.append(next_token)
+        
+        token_str = tokenizer.decode([next_token])
+        elapsed = time.time() - t0
+        print(f"  Step {step}: token={next_token} '{token_str}' ({elapsed:.2f}s)")
+        
+        # Stop on EOS
+        if next_token == tokenizer.eos_token_id:
+            break
+    
+    # ---- Output ----
+    output_text = tokenizer.decode(generated_ids, skip_special_tokens=True)
+    print(f"\n{'='*70}")
+    print(f"Input:  '{PROMPT}'")
+    print(f"Output: '{output_text}'")
+    print(f"{'='*70}")
+    
+    # Verify
+    if "Paris" in output_text or "paris" in output_text.lower():
+        print("✅ PASSED: Model produced 'Paris' — kernel is correct!")
+    else:
+        print(f"⚠️  Model did not produce 'Paris'. Output: {output_text}")
+        print("    This could be due to: missing FFN, missing RoPE, missing mHC,")
+        print("    incomplete weight loading, or other integration gaps.")
+        print("    The kernel FMHA itself is verified separately (cos ≥ 0.999993).")
+
+
+if __name__ == "__main__":
+    main()