#9 Create pip package and automated builds

Release PyPi package + Create GitHub workflow

.github/workflows/build.yaml

@@ -0,0 +1,95 @@
+name: Build AutoAWQ Wheels with CUDA
+
+on:
+  push:
+    tags:
+      - "v*"
+
+jobs:
+  release:
+    # Retrieve tag and create release
+    name: Create Release
+    runs-on: ubuntu-latest
+    outputs:
+      upload_url: ${{ steps.create_release.outputs.upload_url }}
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Extract branch info
+        shell: bash
+        run: |
+          echo "release_tag=${GITHUB_REF#refs/*/}" >> $GITHUB_ENV
+
+      - name: Create Release
+        id: create_release
+        uses: "actions/github-script@v6"
+        env:
+          RELEASE_TAG: ${{ env.release_tag }}
+        with:
+          github-token: "${{ secrets.GITHUB_TOKEN }}"
+          script: |
+            const script = require('.github/workflows/scripts/github_create_release.js')
+            await script(github, context, core)
+
+  build_wheels:
+    name: Build AWQ
+    runs-on: ${{ matrix.os }}
+    needs: release
+
+    strategy:
+      matrix:
+        os: [ubuntu-20.04, windows-latest]
+        pyver: ["3.8", "3.9", "3.10", "3.11"]
+        cuda: ["11.8"]
+    defaults:
+      run:
+        shell: pwsh
+    env:
+        CUDA_VERSION: ${{ matrix.cuda }}
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: actions/setup-python@v3
+        with:
+          python-version: ${{ matrix.pyver }}
+
+      - name: Setup Miniconda
+        uses: conda-incubator/[email protected]
+        with:
+          activate-environment: "build"
+          python-version: ${{ matrix.pyver }}
+          mamba-version: "*"
+          use-mamba: false
+          channels: conda-forge,defaults
+          channel-priority: true
+          add-pip-as-python-dependency: true
+          auto-activate-base: false
+
+      - name: Install Dependencies
+        run: |
+          conda install cuda-toolkit -c "nvidia/label/cuda-${env:CUDA_VERSION}.0"
+          conda install pytorch "pytorch-cuda=${env:CUDA_VERSION}" -c pytorch -c nvidia
+          python -m pip install --upgrade build setuptools wheel ninja
+
+          # Environment variables
+          Add-Content $env:GITHUB_ENV "CUDA_PATH=$env:CONDA_PREFIX"
+          Add-Content $env:GITHUB_ENV "CUDA_HOME=$env:CONDA_PREFIX"
+          if ($IsLinux) {$env:LD_LIBRARY_PATH = $env:CONDA_PREFIX + '/lib:' + $env:LD_LIBRARY_PATH}
+
+          # Print version information
+          python --version
+          python -c "import torch; print('PyTorch:', torch.__version__)"
+          python -c "import torch; print('CUDA:', torch.version.cuda)"
+          python -c "from torch.utils import cpp_extension; print (cpp_extension.CUDA_HOME)"
+
+      - name: Build Wheel
+        run: |
+          python setup.py sdist bdist_wheel
+      
+      - name: Upload Assets
+        uses: shogo82148/actions-upload-release-asset@v1
+        with:
+          upload_url: ${{ needs.release.outputs.upload_url }}
+          asset_path: ./dist/*.whl

.github/workflows/scripts/github_create_release.js

@@ -0,0 +1,17 @@
+module.exports = async (github, context, core) => {
+	try {
+		const response = await github.rest.repos.createRelease({
+			draft: false,
+			generate_release_notes: true,
+			name: process.env.RELEASE_TAG,
+			owner: context.repo.owner,
+			prerelease: false,
+			repo: context.repo.repo,
+			tag_name: process.env.RELEASE_TAG,
+		});
+
+		core.setOutput('upload_url', response.data.upload_url);
+	} catch (error) {
+		core.setFailed(error.message);
+	}
+}

README.md

@@ -4,7 +4,7 @@ AutoAWQ is a package that implements the Activation-aware Weight Quantization (A
 
 Roadmap:
 
-- [ ] Publish pip package
+- [x] Publish pip package
 - [ ] Refactor quantization code
 - [ ] Support more models
 - [ ] Optimize the speed of models
@@ -13,15 +13,29 @@ Roadmap:
 
 Requirements: 
 - Compute Capability 8.0 (sm80). Ampere and later architectures are supported.
+- CUDA Toolkit 11.8 and later.
 
-Clone this repository and install with pip.
+Install:
+- Use pip to install awq
+
+```
+pip install awq
+```
+
+### Build source
+
+<details>
+
+<summary>Build AutoAWQ from scratch</summary>
 
 ```
 git clone https://github.com/casper-hansen/AutoAWQ
 cd AutoAWQ
 pip install -e .
 ```
 
+</details>
+
 ## Supported models
 
 The detailed support list:
@@ -36,6 +50,7 @@ The detailed support list:
 | OPT      | 125m/1.3B/2.7B/6.7B/13B/30B |
 | Bloom    | 560m/3B/7B/                 |
 | LLaVA-v0 | 13B                         |
+| GPTJ     | 6.7B                        |
 
 ## Usage
 
@@ -44,8 +59,8 @@ Below, you will find examples for how to easily quantize a model and run inferen
 ### Quantization
 
 ```python
+from awq import AutoAWQForCausalLM
 from transformers import AutoTokenizer
-from awq.models.auto import AutoAWQForCausalLM
 
 model_path = 'lmsys/vicuna-7b-v1.5'
 quant_path = 'vicuna-7b-v1.5-awq'
@@ -68,8 +83,8 @@ tokenizer.save_pretrained(quant_path)
 Run inference on a quantized model from Huggingface:
 
 ```python
+from awq import AutoAWQForCausalLM
 from transformers import AutoTokenizer
-from awq.models.auto import AutoAWQForCausalLM
 
 quant_path = "casperhansen/vicuna-7b-v1.5-awq"
 quant_file = "awq_model_w4_g128.pt"
@@ -101,8 +116,11 @@ Benchmark speeds may vary from server to server and that it also depends on your
 | MPT-30B     | A6000 | OOM               | 31.57             | --      |
 | Falcon-7B   | A6000 | 39.44             | 27.34             | 1.44x   |
 
+<details>
 
-For example, here is the difference between a fast and slow CPU on MPT-7B:
+<summary>Detailed benchmark (CPU vs. GPU)</summary>
+
+Here is the difference between a fast and slow CPU on MPT-7B:
 
 RTX 4090 + Intel i9 13900K (2 different VMs):
 - CUDA 12.0, Driver 525.125.06: 134 tokens/s (7.46 ms/token)
@@ -113,6 +131,8 @@ RTX 4090 + AMD EPYC 7-Series (3 different VMs):
 - CUDA 12.2, Driver 535.54.03: 56 tokens/s (17.71 ms/token)
 - CUDA 12.0, Driver 525.125.06: 55 tokens/ (18.15 ms/token)
 
+</details>
+
 ## Reference
 
 If you find AWQ useful or relevant to your research, you can cite their [paper](https://arxiv.org/abs/2306.00978):

awq/__init__.py

@@ -0,0 +1 @@
+from awq.models.auto import AutoAWQForCausalLM

awq/entry.py

@@ -4,7 +4,7 @@
 import argparse
 from lm_eval import evaluator
 from transformers import AutoTokenizer
-from awq.models.auto import AutoAWQForCausalLM
+from awq import AutoAWQForCausalLM
 from awq.quantize.auto_clip import apply_clip
 from awq.quantize.auto_scale import apply_scale
 from awq.utils.lm_eval_adaptor import LMEvalAdaptor
@@ -152,7 +152,7 @@ def _warmup(device:str):
     parser.add_argument('--tasks', type=str, default='wikitext', help='Tasks to evaluate. '
                         'Separate tasks by comma for multiple tasks.'
                         'https://github.com/EleutherAI/lm-evaluation-harness/blob/master/docs/task_table.md')
-    parser.add_argument("--task_use_pretrained", default=False, action=argparse.BooleanOptionalAction,
+    parser.add_argument("--task_use_pretrained", default=False, action='store_true',
                         help="Pass '--task_use_pretrained' to use a pretrained model running FP16")
     parser.add_argument('--task_batch_size', type=int, default=1)
     parser.add_argument('--task_n_shot', type=int, default=0)

awq/modules/fused_attn.py

@@ -34,8 +34,6 @@ def _set_cos_sin_cache(self, seq_len, device, dtype):
         sin = freqs.sin()
         cache = torch.cat((cos, sin), dim=-1)
 
-        # self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
-        # self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
         self.register_buffer("cos_sin_cache", cache.half(), persistent=False)
 
     def forward(
@@ -46,7 +44,6 @@ def forward(
     ):
         # Apply rotary embedding to the query and key before passing them
         # to the attention op.
-        # print(positions.shape, query.shape, key.shape, self.cos_sin_cache.shape)
         query = query.contiguous()
         key = key.contiguous()
         awq_inference_engine.rotary_embedding_neox(
@@ -146,7 +143,7 @@ def make_quant_attn(model, dev):
         qweights = torch.cat([q_proj.qweight, k_proj.qweight, v_proj.qweight], dim=1)
         qzeros = torch.cat([q_proj.qzeros, k_proj.qzeros, v_proj.qzeros], dim=1)
         scales = torch.cat([q_proj.scales, k_proj.scales, v_proj.scales], dim=1)
-        # g_idx = torch.cat([q_proj.g_idx, k_proj.g_idx, v_proj.g_idx], dim=0)
+
         g_idx = None
         bias = torch.cat([q_proj.bias, k_proj.bias, v_proj.bias], dim=0) if q_proj.bias is not None else None
 
@@ -156,8 +153,6 @@ def make_quant_attn(model, dev):
         qkv_layer.scales = scales
 
         qkv_layer.bias = bias
-        # We're dropping the rotary embedding layer m.rotary_emb here. We don't need it in the triton branch.
-
         attn = QuantLlamaAttention(m.hidden_size, m.num_heads, qkv_layer, m.o_proj, dev)
 
         if '.' in name:
@@ -169,6 +164,4 @@ def make_quant_attn(model, dev):
             parent = model
             child_name = name
 
-        #print(f"Replacing {name} with quant_attn; parent: {parent_name}, child's name: {child_name}")
-
         setattr(parent, child_name, attn)

awq/modules/fused_mlp.py

@@ -71,7 +71,6 @@ def our_llama_mlp(self, x):
 
 def make_fused_mlp(m, parent_name=''):
     if not hasattr(make_fused_mlp, "called"):
-        # print("[Warning] Calling a fake MLP fusion. But still faster than Huggingface Implimentation.")
         make_fused_mlp.called = True
     """
     Replace all LlamaMLP modules with QuantLlamaMLP modules, which fuses many of the operations.

awq/modules/fused_norm.py

@@ -38,6 +38,4 @@ def make_quant_norm(model):
             parent = model
             child_name = name
 
-        #print(f"Replacing {name} with quant_attn; parent: {parent_name}, child's name: {child_name}")
-
         setattr(parent, child_name, norm)

awq/quantize/auto_scale.py

@@ -1,6 +1,7 @@
 import gc
 import torch
 import torch.nn as nn
+import logging
 
 from transformers.models.bloom.modeling_bloom import BloomBlock, BloomGelu
 from transformers.models.opt.modeling_opt import OPTDecoderLayer
@@ -154,9 +155,8 @@ def _search_module_scale(block, linears2scale: list, x, kwargs={}):
                 best_scales = scales
             block.load_state_dict(org_sd)
         if best_ratio == -1:
-            print(history)
+            logging.debug(history)
             raise Exception
-        # print(best_ratio)
         best_scales = best_scales.view(-1)
 
         assert torch.isnan(best_scales).sum() == 0, best_scales

awq/utils/calib_data.py

@@ -1,4 +1,5 @@
 import torch
+import logging
 from datasets import load_dataset
 
 def get_calib_dataset(data="pileval", tokenizer=None, n_samples=512, block_size=512):
@@ -25,5 +26,5 @@ def get_calib_dataset(data="pileval", tokenizer=None, n_samples=512, block_size=
     # now concatenate all samples and split according to block size
     cat_samples = torch.cat(samples, dim=1)
     n_split = cat_samples.shape[1] // block_size
-    print(f" * Split into {n_split} blocks")
+    logging.debug(f" * Split into {n_split} blocks")
     return [cat_samples[:, i*block_size:(i+1)*block_size] for i in range(n_split)]

awq/utils/lm_eval_adaptor.py

@@ -2,7 +2,7 @@
 import torch
 from lm_eval.base import BaseLM
 import fnmatch
-
+import logging
 
 class LMEvalAdaptor(BaseLM):
 
@@ -52,7 +52,7 @@ def max_length(self):
         elif 'falcon' in self.model_name:
             return 2048
         else:
-            print(self.model.config)
+            logging.debug(self.model.config)
             raise NotImplementedError
 
     @property

awq/utils/parallel.py

@@ -1,6 +1,7 @@
 import os
 import torch
 import gc
+import logging
 
 
 def auto_parallel(args):
@@ -23,5 +24,5 @@ def auto_parallel(args):
         cuda_visible_devices = list(range(8))
     os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(
         [str(dev) for dev in cuda_visible_devices[:n_gpu]])
-    print("CUDA_VISIBLE_DEVICES: ", os.environ["CUDA_VISIBLE_DEVICES"])
+    logging.debug("CUDA_VISIBLE_DEVICES: ", os.environ["CUDA_VISIBLE_DEVICES"])
     return cuda_visible_devices

awq_cuda/layernorm/reduction.cuh

@@ -16,7 +16,7 @@ https://github.com/NVIDIA/FasterTransformer/blob/main/src/fastertransformer/kern
 #include <float.h>
 #include <type_traits>
 
-static const float HALF_FLT_MAX = 65504.F;
+#define HALF_FLT_MAX 65504.F
 #define FINAL_MASK 0xffffffff