examples: use compress_model, remove compress_quantized_weights; update save flow and docs; Fixes #2105

Signed-off-by: zkl-ai <[email protected]>

Author: zkl-ai

examples/quantize_and_pack_int4.ipynb

@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,8 +25,7 @@
     "from compressed_tensors.quantization import (\n",
     "    QuantizationConfig,\n",
     "    QuantizationStatus,\n",
-    "    apply_quantization_config,\n",
-    "    compress_quantized_weights\n",
+    "    apply_quantization_config\n",
     ")\n",
     "from compressed_tensors.compressors import ModelCompressor\n",
     "from transformers import AutoModelForCausalLM, AutoTokenizer, DefaultDataCollator\n",
@@ -37,51 +36,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c883cdc8ecd04866bd01d61796b81c26",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "config.json:   0%|          | 0.00/560 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "32b18b14b6774ce7b61d2854a1ed5f49",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "model.safetensors:   0%|          | 0.00/4.40G [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "370c6d18521a4b65833a411728be1ed7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "generation_config.json:   0%|          | 0.00/129 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
       "text/plain": [
@@ -113,7 +70,7 @@
        ")"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -122,7 +79,7 @@
     "# load a dense, unquantized tiny llama model\n",
     "device = \"cuda:0\"\n",
     "model_name = \"TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T\"\n",
-    "model = AutoModelForCausalLM.from_pretrained(model_name, device_map=device, torch_dtype=\"auto\")\n",
+    "model = AutoModelForCausalLM.from_pretrained(model_name, device_map=device, torch_dtype=torch.bfloat16)\n",
     "model"
    ]
   },
@@ -139,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -164,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -198,14 +155,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Running calibration: 512it [00:33, 15.42it/s]\n"
+      "Running calibration: 512it [00:58,  8.82it/s]\n"
      ]
     }
    ],
@@ -233,20 +190,24 @@
     "\n",
     "Notice that at this point, the weight itself is still a floating point and has not been quantized. \n",
     "\n",
-    "To convert the weights to an integer type, we need to apply the `compress_quantized_weights` function. After compressing the weights, a forward pass of the model can no longer be run in PyTorch"
+    "To convert the weights to an integer type, we need to apply the `compress_model` function. After compressing the weights, a forward pass of the model can no longer be run in PyTorch.\n",
+    "\n",
+    "After compressing the quantized model with the `pack-quantized` format, weights are represented as logical int4 values packed into int32 containers ( `weight_packed` ), with the original shape recorded in `weight_shape`.\n",
+    "\n",
+    "This packed representation is what gets saved to disk when using ModelCompressor.compress_model(model)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Scale: tensor([17296.], device='cuda:4', dtype=torch.float16), Zero Point: tensor([0], device='cuda:4', dtype=torch.int8)\n",
-      "Weight min: -1.587890625 max: 1.0283203125 dtype: torch.float16\n"
+      "Scale: tensor([-3.0465e+26], device='cuda:0', dtype=torch.bfloat16), Zero Point: tensor([0], device='cuda:0', dtype=torch.int8)\n",
+      "Weight min: -1.5859375 max: 1.03125 dtype: torch.bfloat16\n"
      ]
     }
    ],
@@ -262,64 +223,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Compressing model: 154it [00:02, 59.75it/s]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Scale: tensor([17296.], device='cuda:4', dtype=torch.float16), Zero Point: tensor([0], device='cuda:4', dtype=torch.int8)\n",
-      "Weight min: 0 max: 0 dtype: torch.int8\n"
+      "Compressed weight scale: tensor([-3.0465e+26], device='cuda:0', dtype=torch.bfloat16), zero point: tensor([0], device='cuda:0', dtype=torch.int8)\n",
+      "Compressed weight  dtype: torch.int32\n",
+      "Compressed weight shape: torch.Size([2048, 256])\n",
+      "Uncompressed weight shape: tensor([2048, 2048], device='cuda:0')\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
      ]
     }
    ],
    "source": [
     "# convert quantized weights to integers\n",
-    "model.apply(compress_quantized_weights)\n",
+    "compressor = ModelCompressor(quantization_config=config)\n",
+    "compressor.compress_model(model)\n",
     "\n",
     "state_dict = model.state_dict()\n",
     "example_layer = \"model.layers.0.self_attn.q_proj.weight\"\n",
     "scale = state_dict[example_layer + \"_scale\"]\n",
     "zero_point = state_dict[example_layer + \"_zero_point\"]\n",
-    "weight = state_dict[example_layer]\n",
-    "print(f\"Scale: {scale}, Zero Point: {zero_point}\")\n",
-    "print(f\"Weight min: {torch.min(weight)} max: {torch.max(weight)} dtype: {weight.dtype}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After compressing the quantized model, the weight matrix has a range of int4 but is stored in an int8. \n",
-    "\n",
-    "We can further compress the model on disk using the `pack-quantized` format we specified in the config. This compression format will pack the int4 weights into int32"
+    "weight = state_dict[example_layer + \"_packed\"]\n",
+    "shape = state_dict[example_layer + \"_shape\"]\n",
+    "print(f\"Compressed weight scale: {scale}, zero point: {zero_point}\")\n",
+    "print(f\"Compressed weight  dtype: {weight.dtype}\")\n",
+    "print(f\"Compressed weight shape: {weight.shape}\")\n",
+    "print(f\"Uncompressed weight shape: {shape}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Compression format: pack-quantized\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Quantized Compression: 100%|██████████| 509/509 [00:03<00:00, 153.70it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Size of the model's weights on disk using safetensors: 712.23 MB\n"
+      "Compression format: pack-quantized\n",
+      "Size of the model's weights on disk using safetensors: 712.25 MB\n"
      ]
     }
    ],
@@ -330,9 +289,8 @@
     "compression_format = config.format\n",
     "print(f\"Compression format: {compression_format}\")\n",
     "\n",
-    "compressor = ModelCompressor(quantization_config=config)\n",
-    "compressed_state_dict = compressor.compress(model)\n",
-    "model.save_pretrained(output_dir, state_dict=compressed_state_dict)\n",
+    "\n",
+    "model.save_pretrained(output_dir, state_dict=model.state_dict())\n",
     "compressor.update_config(output_dir)\n",
     "\n",
     "compressed_size_on_disk_mb = os.path.getsize(os.path.join(output_dir, \"model.safetensors\")) / 1024 / 1024\n",
@@ -356,7 +314,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.12"
   }
  },
  "nbformat": 4,