Add MFU formula as reference material (#395)

* add MFU formula as reference material

* Update previous_chapters.py

ch04/02_performance-analysis/flops-analysis.ipynb

@@ -53,19 +53,27 @@
      "output_type": "stream",
      "text": [
       "thop version: 0.1.1-2209072238\n",
-      "torch version: 2.2.2\n",
-      "tiktoken version: 0.5.1\n"
+      "torch version: 2.2.1+cu121\n"
      ]
     }
    ],
    "source": [
     "from importlib.metadata import version\n",
     "\n",
-    "import matplotlib\n",
-    "import torch\n",
-    "\n",
-    "print(\"thop version:\", version(\"thop\"))\n",
-    "print(\"torch version:\", version(\"torch\"))"
+    "pkgs = [\n",
+    "    \"thop\",\n",
+    "    \"torch\",\n",
+    "]\n",
+    "for p in pkgs:\n",
+    "    print(f\"{p} version: {version(p)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " \n",
+    "# Simple benchmark with fixed batch size"
    ]
   },
   {
@@ -112,7 +120,8 @@
     "}\n",
     "\n",
     "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "input_tensor = torch.randint(0, 50257, (2, 1024)).to(device)\n",
+    "batch_size = 2\n",
+    "input_tensor = torch.randint(0, 50257, (batch_size, 1024)).to(device)\n",
     "\n",
     "for size in model_configs:\n",
     "    BASE_CONFIG.update(model_configs[size])\n",
@@ -129,6 +138,343 @@
     "    del model\n",
     "    torch.cuda.empty_cache()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " \n",
+    "# Simple benchmark with automatic batch size finding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Processing gpt-small (124M)\n",
+      "  Batch size 128: 3.2e+13 FLOPS\n",
+      "  Batch size 160: 4.0e+13 FLOPS\n",
+      "  Batch size 176: 4.5e+13 FLOPS\n",
+      "  Batch size 184: 4.7e+13 FLOPS\n",
+      "  Batch size 186: 4.7e+13 FLOPS\n",
+      "\n",
+      "Processing gpt-medium (355M)\n",
+      "  Batch size 128: 9.3e+13 FLOPS\n",
+      "  Batch size 136: 9.8e+13 FLOPS\n",
+      "  Batch size 140: 1.0e+14 FLOPS\n",
+      "  Batch size 142: 1.0e+14 FLOPS\n",
+      "  Batch size 143: 1.0e+14 FLOPS\n",
+      "\n",
+      "Processing gpt-large (774M)\n",
+      "  Batch size 128: 2.0e+14 FLOPS\n",
+      "\n",
+      "Processing gpt-xl (1558M)\n",
+      "  Batch size 64: 2.0e+14 FLOPS\n",
+      "  Batch size 96: 3.1e+14 FLOPS\n"
+     ]
+    }
+   ],
+   "source": [
+    "for size in model_configs:\n",
+    "    print(f\"\\nProcessing {size}\")\n",
+    "    config = BASE_CONFIG.copy()\n",
+    "    config.update(model_configs[size])\n",
+    "\n",
+    "    min_batch_size = 1\n",
+    "    max_batch_size = None\n",
+    "    max_possible_batch_size = 4096\n",
+    "\n",
+    "    while min_batch_size <= max_possible_batch_size:\n",
+    "        batch_size = (min_batch_size + max_possible_batch_size) // 2\n",
+    "        try:\n",
+    "            input_tensor = torch.randint(\n",
+    "                0, config[\"vocab_size\"],\n",
+    "                (batch_size, config[\"context_length\"]),\n",
+    "                device=device\n",
+    "            )\n",
+    "\n",
+    "            model = GPTModel(config).bfloat16().to(device)\n",
+    "\n",
+    "            # MACS = multiply-accumulate operations\n",
+    "            # MACS are typically counted as two FLOPS (one multiply and one accumulate)\n",
+    "            macs, params = profile(model, inputs=(input_tensor,), verbose=False)\n",
+    "            flops = 2 * macs\n",
+    "            print(f\"  Batch size {batch_size}: {flops:.1e} FLOPS\")\n",
+    "\n",
+    "            # If successful, try a larger batch size\n",
+    "            min_batch_size = batch_size + 1\n",
+    "            max_batch_size = batch_size\n",
+    "\n",
+    "            # Clean up\n",
+    "            del model, input_tensor\n",
+    "            torch.cuda.empty_cache()\n",
+    "\n",
+    "        except RuntimeError as e:\n",
+    "            if \"out of memory\" in str(e):\n",
+    "                # Try smaller batch size\n",
+    "                max_possible_batch_size = batch_size - 1\n",
+    "\n",
+    "                # Clean up\n",
+    "                try:\n",
+    "                    del model, input_tensor\n",
+    "                    torch.cuda.empty_cache()\n",
+    "                except NameError:\n",
+    "                    pass\n",
+    "            else:\n",
+    "                raise e"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "&nbsp;\n",
+    "# Benchmark with automatic batch size finding and Model FLOP Utilization (MFU)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Model FLOPs Utilization (MFU) explanation from the [PaLM paper](https://arxiv.org/abs/2204.02311)\n",
+    "\n",
+    "> We propose a new metric for efficiency that is implementation-independent and permits a cleaner comparison of system efficiency, called model FLOPs utilization (MFU). This is the ratio of the observed throughput (tokens-per-second) relative to the theoretical maximum throughput of a system operating at peak FLOPs. Crucially, the “theoretical maximum” throughput only accounts for the required operations to compute the forward+backward passes, and not rematerialization.\n",
+    "\n",
+    "\n",
+    "$$\\text{MFU} = \\frac{\\text{Observed Tokens per Second}}{\\text{Theoretical Max Tokens per Second}}$$\n",
+    "\n",
+    "where \n",
+    "\n",
+    "$$\\text{Theoretical Max Tokens per Second} = \\frac{\\text{Max FLOPs per Second}}{\\text{Total FLOPs per Token}}$$\n",
+    "\n",
+    "and\n",
+    "\n",
+    "$$\\text{Tokens per Second} = \\frac{\\text{Batch Size} \\times \\text{Sequence Length}}{\\text{Total Time}}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Max flops per second provided by the GPU manufacturer\n",
+    "\n",
+    "flops_per_second = {\n",
+    "    \"H100\": {\n",
+    "        torch.float32: 60e12,  # 60 TFLOPs for FP32 on NVIDIA H100\n",
+    "        torch.float16: 1.979e15,  # 1979 TFLOPs for FP16 on NVIDIA H100\n",
+    "        torch.bfloat16: 1.979e15\n",
+    "    },\n",
+    "    \"L4\": {\n",
+    "        torch.float32: 15e12,  # 15 TFLOPs for FP32 on NVIDIA L4\n",
+    "        torch.float16: 30e12,   # 30 TFLOPs for FP16 on NVIDIA L4\n",
+    "        torch.bfloat16: 30e12 \n",
+    "    },\n",
+    "    \"T4\": {\n",
+    "        torch.float32: 8.1e12,  # 8.1 TFLOPs for FP32 on NVIDIA T4\n",
+    "        torch.float16: 130e12,  # 130 TFLOPs for FP16 on NVIDIA T4\n",
+    "        torch.bfloat16: 130e12\n",
+    "    },\n",
+    "    \"A10G\": {\n",
+    "        torch.float32: 15.6e12,  # 15.6 TFLOPs for FP32 on NVIDIA A10G\n",
+    "        torch.float16: 78e12,    # 78 TFLOPs for FP16 on NVIDIA A10G\n",
+    "        torch.bfloat16: 78e12\n",
+    "    },\n",
+    "    \"A100\": {\n",
+    "        torch.float32: 19.5e12,  # 19.5 TFLOPs for FP32 on NVIDIA A100\n",
+    "        torch.float16: 1.248e15, # 1248 TFLOPs for FP16 on NVIDIA A100\n",
+    "        torch.bfloat16: 1.248e15\n",
+    "    },\n",
+    "    \"H200\": {\n",
+    "        torch.float32: 70e12,    # 70 TFLOPs for FP32 on NVIDIA H200\n",
+    "        torch.float16: 1.2e15,   # Assuming 1200 TFLOPs for FP16 on NVIDIA H200\n",
+    "        torch.bfloat16: 1.2e15\n",
+    "    },\n",
+    "    \"RTX_3080\": {\n",
+    "        torch.float32: 29.8e12,  # 29.8 TFLOPs for FP32 on NVIDIA RTX 3080\n",
+    "        torch.float16: 59.6e12,  # 59.6 TFLOPs for FP16 on NVIDIA RTX 3080\n",
+    "        torch.bfloat16: 59.6e12\n",
+    "    },\n",
+    "    \"RTX_3090\": {\n",
+    "        torch.float32: 35.6e12,  # 35.6 TFLOPs for FP32 on NVIDIA RTX 3090\n",
+    "        torch.float16: 71.2e12,  # 71.2 TFLOPs for FP16 on NVIDIA RTX 3090\n",
+    "        torch.bfloat16: 71.2e12\n",
+    "    },\n",
+    "    \"GTX_1080\": {\n",
+    "        torch.float32: 8.9e12,   # 8.9 TFLOPs for FP32 on NVIDIA GTX 1080\n",
+    "        torch.float16: 8.9e12,   # No dedicated FP16 performance; using FP32 value\n",
+    "        torch.bfloat16: 8.9e12\n",
+    "    },\n",
+    "    \"GTX_1080Ti\": {\n",
+    "        torch.float32: 11.3e12,  # 11.3 TFLOPs for FP32 on NVIDIA GTX 1080Ti\n",
+    "        torch.float16: 11.3e12,  # No dedicated FP16 performance; using FP32 value\n",
+    "        torch.bfloat16: 11.3e12\n",
+    "    },\n",
+    "    \"GTX_1660\": {\n",
+    "        torch.float32: 5e12,     # 5 TFLOPs for FP32 on NVIDIA GTX 1660\n",
+    "        torch.float16: 5e12,     # No dedicated FP16 performance; using FP32 value\n",
+    "        torch.bfloat16: 5e12\n",
+    "    },\n",
+    "    \"GTX_1660Ti\": {\n",
+    "        torch.float32: 5.5e12,   # 5.5 TFLOPs for FP32 on NVIDIA GTX 1660Ti\n",
+    "        torch.float16: 5.5e12,   # No dedicated FP16 performance; using FP32 value\n",
+    "        torch.bfloat16: 5.5e12\n",
+    "    }\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPU Model: L4\n",
+      "\n",
+      "Processing gpt-small (124M)\n",
+      "  Batch size 8: Tokens/sec: 14488.21, MFU: 0.3580\n",
+      "  Batch size 12: Tokens/sec: 15378.16, MFU: 0.3799\n",
+      "\n",
+      "Processing gpt-medium (355M)\n",
+      "  Batch size 2: Tokens/sec: 6493.81, MFU: 0.4591\n",
+      "  Batch size 3: Tokens/sec: 6328.82, MFU: 0.4474\n",
+      "\n",
+      "Processing gpt-large (774M)\n",
+      "  Batch size 4: Tokens/sec: 3130.38, MFU: 0.4834\n",
+      "\n",
+      "Processing gpt-xl (1558M)\n",
+      "  Batch size 2: Tokens/sec: 1896.17, MFU: 0.5897\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "def get_gpu_model(flops_per_second_dict):\n",
+    "    device_name = torch.cuda.get_device_name(0)\n",
+    "    for model in flops_per_second_dict.keys():\n",
+    "        if model in device_name:\n",
+    "            return model\n",
+    "    return \"Unknown\"  # Default if no matching model is found\n",
+    "\n",
+    "\n",
+    "gpu_model = get_gpu_model(flops_per_second)\n",
+    "print(\"GPU Model:\", gpu_model)\n",
+    "\n",
+    "if gpu_model != \"Unknown\":\n",
+    "\n",
+    "    for size in model_configs:\n",
+    "        print(f\"\\nProcessing {size}\")\n",
+    "        config = BASE_CONFIG.copy()\n",
+    "        config.update(model_configs[size])\n",
+    "\n",
+    "        min_batch_size = 1\n",
+    "        max_batch_size = None\n",
+    "        max_possible_batch_size = 4096\n",
+    "\n",
+    "        while min_batch_size <= max_possible_batch_size:\n",
+    "            batch_size = (min_batch_size + max_possible_batch_size) // 2\n",
+    "            try:\n",
+    "                input_tensor = torch.randint(\n",
+    "                    0, config[\"vocab_size\"],\n",
+    "                    (batch_size, config[\"context_length\"]),\n",
+    "                    device=device\n",
+    "                )\n",
+    "\n",
+    "                model = GPTModel(config).bfloat16().to(device)\n",
+    "                model.train()\n",
+    "\n",
+    "                # Start timing\n",
+    "                torch.cuda.synchronize()\n",
+    "                start_time = time.time()\n",
+    "\n",
+    "                # Forward & backward pass\n",
+    "                output = model(input_tensor)\n",
+    "                loss = output.sum()  # Compute a dummy loss \n",
+    "                loss.backward()\n",
+    "\n",
+    "                # End timing\n",
+    "                torch.cuda.synchronize()\n",
+    "                end_time = time.time()\n",
+    "\n",
+    "                total_time_seconds = end_time - start_time\n",
+    "\n",
+    "                # Calculate FLOPs for forward pass\n",
+    "                macs, params = profile(model, inputs=(input_tensor,), verbose=False)\n",
+    "                flops_forward = 2 * macs  # Assuming one MAC equals two FLOPs\n",
+    "\n",
+    "                # Estimate FLOPs for backward pass (typically 2x forward FLOPs)\n",
+    "                flops_backward = 2 * flops_forward\n",
+    "\n",
+    "                # Total FLOPs for forward + backward passes\n",
+    "                total_flops = flops_forward + flops_backward  # Or total_flops = flops_forward * 3\n",
+    "\n",
+    "                data_type = next(model.parameters()).dtype\n",
+    "                max_flops_per_second = flops_per_second[gpu_model].get(data_type, 0)\n",
+    "\n",
+    "                # Compute tokens per second\n",
+    "                tokens_processed = batch_size * config[\"context_length\"]\n",
+    "                tokens_per_second = tokens_processed / total_time_seconds\n",
+    "\n",
+    "                # Compute FLOPs per token\n",
+    "                flops_per_token = total_flops / tokens_processed\n",
+    "\n",
+    "                # Compute theoretical max tokens per second\n",
+    "                if flops_per_token > 0:\n",
+    "                    theoretical_max_tokens_per_second = max_flops_per_second / flops_per_token\n",
+    "                else:\n",
+    "                    theoretical_max_tokens_per_second = 0  # Avoid division by zero\n",
+    "\n",
+    "                # Compute MFU\n",
+    "                if theoretical_max_tokens_per_second > 0:\n",
+    "                    mfu = tokens_per_second / theoretical_max_tokens_per_second\n",
+    "                else:\n",
+    "                    mfu = 0  # Avoid division by zero\n",
+    "\n",
+    "                print(f\"  Batch size {batch_size}: Tokens/sec: {tokens_per_second:.2f}, MFU: {mfu:.4f}\")\n",
+    "\n",
+    "                # If successful, try a larger batch size\n",
+    "                min_batch_size = batch_size + 1\n",
+    "                max_batch_size = batch_size\n",
+    "\n",
+    "                # Clean up\n",
+    "                del model, input_tensor, output, loss\n",
+    "                torch.cuda.empty_cache()\n",
+    "\n",
+    "            except RuntimeError as e:\n",
+    "                if \"out of memory\" in str(e).lower():\n",
+    "                    # Try smaller batch size\n",
+    "                    max_possible_batch_size = batch_size - 1\n",
+    "\n",
+    "                    # Clean up\n",
+    "                    try:\n",
+    "                        del model, input_tensor\n",
+    "                        torch.cuda.empty_cache()\n",
+    "                    except NameError:\n",
+    "                        pass\n",
+    "                else:\n",
+    "                    raise e\n",
+    "\n",
+    "else:\n",
+    "    print(\"Unknown GPU model. Please update the flops_per_second dictionary with your GPU information.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Note that the batch sizes are smaller than previously because we also carry out the backward pass here, which is more memory-intensive"
+   ]
   }
  ],
  "metadata": {

ch04/02_performance-analysis/previous_chapters.py

@@ -6,52 +6,8 @@
 # This file collects all the relevant code that we covered thus far
 # throughout Chapters 2-4.
 # This file can be run as a standalone script.
-
-import tiktoken
 import torch
 import torch.nn as nn
-from torch.utils.data import Dataset, DataLoader
-
-#####################################
-# Chapter 2
-#####################################
-
-
-class GPTDatasetV1(Dataset):
-    def __init__(self, txt, tokenizer, max_length, stride):
-        self.input_ids = []
-        self.target_ids = []
-
-        # Tokenize the entire text
-        token_ids = tokenizer.encode(txt, allowed_special={"<|endoftext|>"})
-
-        # Use a sliding window to chunk the book into overlapping sequences of max_length
-        for i in range(0, len(token_ids) - max_length, stride):
-            input_chunk = token_ids[i:i + max_length]
-            target_chunk = token_ids[i + 1: i + max_length + 1]
-            self.input_ids.append(torch.tensor(input_chunk))
-            self.target_ids.append(torch.tensor(target_chunk))
-
-    def __len__(self):
-        return len(self.input_ids)
-
-    def __getitem__(self, idx):
-        return self.input_ids[idx], self.target_ids[idx]
-
-
-def create_dataloader_v1(txt, batch_size=4, max_length=256,
-                         stride=128, shuffle=True, drop_last=True, num_workers=0):
-    # Initialize the tokenizer
-    tokenizer = tiktoken.get_encoding("gpt2")
-
-    # Create dataset
-    dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)
-
-    # Create dataloader
-    dataloader = DataLoader(
-        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=num_workers)
-
-    return dataloader
 
 
 #####################################
@@ -236,44 +192,3 @@ def generate_text_simple(model, idx, max_new_tokens, context_size):
         idx = torch.cat((idx, idx_next), dim=1)  # (batch, n_tokens+1)
 
     return idx
-
-
-if __name__ == "__main__":
-
-    GPT_CONFIG_124M = {
-        "vocab_size": 50257,     # Vocabulary size
-        "context_length": 1024,  # Context length
-        "emb_dim": 768,          # Embedding dimension
-        "n_heads": 12,           # Number of attention heads
-        "n_layers": 12,          # Number of layers
-        "drop_rate": 0.1,        # Dropout rate
-        "qkv_bias": False        # Query-Key-Value bias
-    }
-
-    torch.manual_seed(123)
-    model = GPTModel(GPT_CONFIG_124M)
-    model.eval()  # disable dropout
-
-    start_context = "Hello, I am"
-
-    tokenizer = tiktoken.get_encoding("gpt2")
-    encoded = tokenizer.encode(start_context)
-    encoded_tensor = torch.tensor(encoded).unsqueeze(0)
-
-    print(f"\n{50*'='}\n{22*' '}IN\n{50*'='}")
-    print("\nInput text:", start_context)
-    print("Encoded input text:", encoded)
-    print("encoded_tensor.shape:", encoded_tensor.shape)
-
-    out = generate_text_simple(
-        model=model,
-        idx=encoded_tensor,
-        max_new_tokens=10,
-        context_size=GPT_CONFIG_124M["context_length"]
-    )
-    decoded_text = tokenizer.decode(out.squeeze(0).tolist())
-
-    print(f"\n\n{50*'='}\n{22*' '}OUT\n{50*'='}")
-    print("\nOutput:", out)
-    print("Output length:", len(out[0]))
-    print("Output text:", decoded_text)