Add experimental INT8 quantized training

benchmarks/quantized_training/pretrain_llama2.py

@@ -0,0 +1,150 @@
+# pre-train a mini Llama2 on TinyStories with INT8 quantized training
+# pip install transformers sentencepiece wandb
+#
+# BF16 baseline: python benchmarks/quantized_training/pretrain_llama2.py --seed 2024 --n_steps 10_000 --compile
+# INT8 QT:       python benchmarks/quantized_training/pretrain_llama2.py --seed 2024 --n_steps 10_000 --compile --quantize int8_weight_only
+
+import os
+
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+
+import argparse
+from pathlib import Path
+
+import numpy as np
+import torch
+import wandb
+from tqdm import tqdm
+from transformers import LlamaConfig, LlamaForCausalLM
+
+from torchao.prototype import low_bit_optim
+from torchao.prototype.quantized_training import int8_weight_only_quantized_training
+from torchao.quantization.quant_api import quantize_
+
+
+def get_loss(model: LlamaForCausalLM, batch: torch.Tensor):
+    return model(batch, labels=batch).loss
+
+
+def get_tinystories():
+    save_path = Path("tinystories.bin")
+
+    if not save_path.exists():
+        import sentencepiece as spm
+        from huggingface_hub import hf_hub_download
+
+        tokenizer_path = hf_hub_download("meta-llama/Llama-2-7b", "tokenizer.model")
+        tokenizer = spm.SentencePieceProcessor(tokenizer_path)
+        assert tokenizer.vocab_size() < (1 << 16)  # make sure we can use uint16
+
+        # do everything in memory. we have enough RAM
+        filepath = hf_hub_download(
+            "roneneldan/TinyStories",
+            "TinyStoriesV2-GPT4-train.txt",
+            repo_type="dataset",
+        )
+        stories = open(filepath).read().split("\n<|endoftext|>\n")
+
+        tokens_list = []
+        chunk_size = 10_000
+        for i in tqdm(range(0, len(stories), chunk_size), desc="Tokenizing TinyStories"):
+            chunk = stories[i : min(i + chunk_size, len(stories))]
+            tokens_list.extend(tokenizer.Encode(chunk, add_bos=True, add_eos=True, num_threads=4))
+
+        total_size = sum(len(x) for x in tokens_list)
+        mmap_tokens = np.memmap(save_path, dtype=np.uint16, mode="w+", shape=total_size)
+        i = 0
+        for tokens in tokens_list:
+            mmap_tokens[i : i + len(tokens)] = tokens
+            i += len(tokens)
+        mmap_tokens.flush()
+
+    tokens = np.memmap(save_path, dtype=np.uint16, mode="r")
+    return torch.from_numpy(tokens)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # default config is 470M
+    parser.add_argument("--d_model", type=int, default=1024)
+    parser.add_argument("--depth", type=int, default=24)
+    parser.add_argument("--ffn_size", type=int, default=4096)
+    parser.add_argument("--head_dim", type=int, default=64)
+
+    parser.add_argument("--quantize")
+    parser.add_argument("--activation_checkpointing", action="store_true")
+    parser.add_argument("--compile", action="store_true")
+
+    parser.add_argument("--n_steps", type=int, default=1000)
+    parser.add_argument("--batch_size", type=int, default=4)
+    parser.add_argument("--seq_len", type=int, default=2048)
+
+    parser.add_argument("--optim", default="AdamW")
+    parser.add_argument("--lr", type=float, default=3e-4)
+    parser.add_argument("--weight_decay", type=float, default=1e-2)
+
+    parser.add_argument("--project", default="int8_quantized_training")
+    parser.add_argument("--run_name")
+    parser.add_argument("--seed", type=int)
+    args = parser.parse_args()
+
+    if args.seed is not None:
+        torch.manual_seed(args.seed)
+
+    config = LlamaConfig(
+        hidden_size=args.d_model,
+        intermediate_size=args.ffn_size,
+        num_hidden_layers=args.depth,
+        num_attention_heads=args.d_model // args.head_dim,
+        max_position_embeddings=args.seq_len,
+        use_cache=False,
+    )
+    model = LlamaForCausalLM(config).bfloat16().cuda()
+    if args.activation_checkpointing:
+        model.gradient_checkpointing_enable()
+    if args.quantize == "int8_weight_only":
+        quantize_(model, int8_weight_only_quantized_training(), set_inductor_config=False)
+    elif args.quantize is not None:
+        raise ValueError(f"Unsupported quantize={args.quantize}")
+    print(f"No. of params: {sum(p.numel() for p in model.parameters()):,}")
+    print(f"No. of buffers: {sum(p.numel() for p in model.buffers()):,}")
+
+    # only use optimizers from torchao.prototype.low_bit_optim to support quantized training
+    if args.optim == "AdamW":
+        args.optim = "_AdamW"
+    optim = getattr(low_bit_optim, args.optim)(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
+
+    data = get_tinystories().cuda()
+    run = wandb.init(dir="/tmp", config=args, project=args.project, name=args.run_name)
+
+    step = 0
+    log_interval = 50
+    pbar = tqdm(total=args.n_steps, dynamic_ncols=True)
+    model.train()
+    _get_loss = torch.compile(get_loss) if args.compile else get_loss
+
+    while step < args.n_steps:
+        # randomly select a continuous chunk, then reshape it
+        idx = torch.randint(0, data.shape[0] - args.batch_size * args.seq_len, (1,)).item()
+        batch = data[idx : idx + args.batch_size * args.seq_len].view(args.batch_size, args.seq_len).long()
+
+        loss = _get_loss(model, batch)
+        loss.backward()
+
+        if step % log_interval == 0:
+            log_dict = dict(
+                loss=loss.item(),
+                lr=optim.param_groups[0]["lr"],
+                max_memory_allocated=torch.cuda.max_memory_allocated() / 1e9,
+                max_memory_active=torch.cuda.memory_stats().get("active_bytes.all.peak", 0) / 1e9,
+            )
+            run.log(log_dict, step=step)
+            pbar.set_postfix(loss=log_dict["loss"])
+
+        optim.step()
+        optim.zero_grad()
+
+        step += 1
+        pbar.update()
+
+    run.finish()

test/prototype/test_quantized_training.py

@@ -0,0 +1,225 @@
+import copy
+
+import pytest
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
+from torch.testing._internal.common_fsdp import FSDPTest
+from torch.testing._internal.common_utils import TestCase, instantiate_parametrized_tests, parametrize, run_tests
+
+from torchao.prototype.low_bit_optim import _AdamW
+from torchao.prototype.quantized_training import Int8QTLinearWeight, int8_weight_only_quantized_training
+from torchao.quantization.quant_api import quantize_
+from torchao.utils import TORCH_VERSION_AFTER_2_3, TORCH_VERSION_AFTER_2_4
+
+if not TORCH_VERSION_AFTER_2_3:
+    pytest.skip("Requires torch>=2.4", allow_module_level=True)
+
+
+_DEVICES = ["cpu"] + (["cuda"] if torch.cuda.is_available() else [])
+
+
+def _reset():
+    # using TF32 will cause mixed mm to segfault with triton backend
+    # fixed in nightly by https://github.com/pytorch/pytorch/pull/133173
+    # also required for correctness check
+    torch.set_float32_matmul_precision("highest")
+    torch._dynamo.reset()
+
+
+# we always use `quantize_(set_inductor_config=False)` to reduce compile time in CI.
+class TestQuantizedTraining(TestCase):
+    @parametrize("device", _DEVICES)
+    def test_int8_stochastic_rounding(self, device):
+        x = torch.randn(32, device=device)
+        x_samples = x.view(1, -1).repeat(100_000, 1)
+
+        x_int8, x_scale = Int8QTLinearWeight.quantize(x_samples, stochastic_rounding=True)
+        x_dequant_samples = x_int8 * x_scale.view(-1, 1)
+        x_dequant_mean = x_dequant_samples.mean(0)
+
+        # a more rigorous test would be to do a hypothesis testing.
+        # due to the statistical nature, this assertion may still fail, though very rarely.
+        torch.testing.assert_close(x_dequant_mean, x, atol=1e-4, rtol=1e-4)
+
+    @parametrize("leading_dims", [(), (2,), (2, 4)])
+    @parametrize("bias", [False, True])
+    @parametrize("device", _DEVICES)
+    def test_int8_linear(self, leading_dims, bias, device):
+        _reset()
+        embed_dim = 32
+
+        linear_fp32 = nn.Linear(embed_dim, embed_dim, bias=bias, device=device)
+        linear_int8 = copy.deepcopy(linear_fp32)
+        quantize_(linear_int8, int8_weight_only_quantized_training(), set_inductor_config=False)
+        linear_fp32.weight.data = linear_int8.weight.data.dequantize()
+
+        input_fp32 = torch.randn(leading_dims + (embed_dim,), device=device)
+        input_int8 = input_fp32.clone()
+        input_fp32.requires_grad_(True)
+        input_int8.requires_grad_(True)
+
+        # test forward
+        out_fp32 = linear_fp32(input_fp32)
+        out_int8 = linear_int8(input_int8)
+        torch.testing.assert_close(out_fp32, out_int8)
+
+        # test backward
+        grad = torch.randn(leading_dims + (embed_dim,), device=device)
+        out_fp32.backward(grad)
+        out_int8.backward(grad)
+        torch.testing.assert_close(input_fp32.grad, input_int8.grad)
+        torch.testing.assert_close(linear_fp32.weight.grad, linear_int8.weight.grad)
+        if bias:
+            torch.testing.assert_close(linear_fp32.bias.grad, linear_int8.bias.grad)
+
+    @parametrize("leading_dims", [(), (2,), (2, 4)])
+    @parametrize("bias", [False, True])
+    @parametrize("device", _DEVICES)
+    def test_int8_linear_compile(self, leading_dims, bias, device):
+        _reset()
+        embed_dim = 128
+
+        linear_eager = nn.Linear(embed_dim, embed_dim, bias=bias, device=device)
+        quantize_(linear_eager, int8_weight_only_quantized_training(), set_inductor_config=False)
+        linear_compiled = copy.deepcopy(linear_eager)
+        linear_compiled.compile()
+
+        input_eager = torch.randn(leading_dims + (embed_dim,), device=device) * 10
+        input_compiled = input_eager.clone()
+        input_eager.requires_grad_(True)
+        input_compiled.requires_grad_(True)
+
+        out_eager = linear_eager(input_eager)
+        out_compiled = linear_compiled(input_compiled)
+        torch.testing.assert_close(out_eager, out_compiled)
+
+        grad = torch.randn(leading_dims + (embed_dim,), device=device)
+        out_eager.backward(grad)
+        out_compiled.backward(grad)
+        torch.testing.assert_close(input_eager.grad, input_compiled.grad)
+        torch.testing.assert_close(linear_eager.weight.grad, linear_compiled.weight.grad)
+        if bias:
+            torch.testing.assert_close(linear_eager.bias.grad, linear_compiled.bias.grad)
+
+    @parametrize("compile", [False, True])
+    @parametrize("device", _DEVICES)
+    def test_int8_linear_training(self, compile, device):
+        _reset()
+        bsize = 4
+        embed_dim = 32
+        n_classes = 10
+
+        model_fp32 = nn.Sequential(
+            nn.Linear(embed_dim, embed_dim * 2, bias=False),
+            nn.GELU(),
+            nn.Linear(embed_dim * 2, n_classes),
+        ).to(device)
+        model_int8 = copy.deepcopy(model_fp32)
+        # don't set inductor flags to speed up CI time
+        quantize_(model_int8, int8_weight_only_quantized_training(), set_inductor_config=False)
+
+        if compile:
+            model_fp32.compile()
+            model_int8.compile()
+
+        optim_fp32 = _AdamW(model_fp32.parameters())
+        optim_int8 = _AdamW(model_int8.parameters())
+
+        for _ in range(5):
+            inputs = torch.randn(bsize, embed_dim, device=device)
+            labels = torch.randint(n_classes, size=(bsize,), device=device)
+            loss_fp32 = F.cross_entropy(model_fp32(inputs), labels)
+            loss_int8 = F.cross_entropy(model_int8(inputs), labels)
+
+            rel_error = abs(loss_int8.item() - loss_fp32.item()) / abs(loss_fp32.item())
+            assert rel_error < 2e-3, rel_error
+
+            loss_fp32.backward()
+            optim_fp32.step()
+            optim_fp32.zero_grad()
+
+            loss_int8.backward()
+            optim_int8.step()
+            optim_int8.zero_grad()
+
+
+class TestFSDP2(FSDPTest):
+    @property
+    def world_size(self) -> int:
+        return 2
+
+    @skip_if_lt_x_gpu(2)
+    def test_fsdp2(self):
+        # FSDP2 + compiled quantized training fails with PyTorch 2.4
+        compile_layer_choices = [False]
+        if TORCH_VERSION_AFTER_2_4:
+            compile_layer_choices.append(True)
+
+        self.run_subtests(
+            {"compile_layer": compile_layer_choices},
+            self._test_fsdp2,
+        )
+
+    def _test_fsdp2(self, compile_layer):
+        import torch.distributed as dist
+        from torch.distributed._composable.fsdp import fully_shard
+        from torch.testing._internal.distributed._tensor.common_dtensor import ModelArgs, Transformer
+
+        _reset()
+        batch_size = 3
+        vocab_size = 32
+        seq_len = 64
+        model_args = ModelArgs(
+            n_layers=2,
+            n_heads=2,
+            dim=128,
+            vocab_size=vocab_size,
+            max_seq_len=seq_len,
+            dropout_p=0,
+        )
+        torch.manual_seed(42)
+        base_model = Transformer(model_args).cuda()
+        quantize_(base_model, int8_weight_only_quantized_training(), set_inductor_config=False)
+        fsdp_model = copy.deepcopy(base_model)
+
+        if compile_layer:
+            for layer in base_model.layers:
+                layer.compile()
+
+        for layer in fsdp_model.layers:
+            if compile_layer:
+                layer.compile()
+            fully_shard(layer)
+        fully_shard(fsdp_model)
+
+        base_optim = torch.optim.Adam(base_model.parameters(), lr=1e-2, foreach=False, fused=False)
+        fsdp_optim = torch.optim.Adam(fsdp_model.parameters(), lr=1e-2, foreach=False, fused=False)
+
+        torch.manual_seed(42 + self.rank + 1)
+        for iter_idx in range(5):
+            inp = torch.randint(0, vocab_size, (batch_size, seq_len), device="cuda")
+            fsdp_optim.zero_grad(set_to_none=(iter_idx % 2 == 0))
+            fsdp_loss = fsdp_model(inp).sum()
+            fsdp_loss.backward()
+            fsdp_optim.step()
+
+            base_optim.zero_grad(set_to_none=(iter_idx % 2 == 0))
+            base_loss = base_model(inp).sum()
+            base_loss.backward()
+            for param in base_model.parameters():
+                if param.grad is not None:
+                    dist.all_reduce(param.grad, op=dist.ReduceOp.AVG)
+            base_optim.step()
+
+            # due to stochastic rounding, use a pretty large tolerance here
+            rel_error = (fsdp_loss - base_loss).abs() / base_loss.abs()
+            assert rel_error < 0.05, rel_error
+
+
+instantiate_parametrized_tests(TestQuantizedTraining)
+
+
+if __name__ == "__main__":
+    run_tests()

torchao/prototype/low_bit_optim/__init__.py

@@ -1,3 +1,3 @@
 from .adam import Adam8bit, Adam4bit, AdamFp8
-from .adamw import AdamW8bit, AdamW4bit, AdamWFp8
+from .adamw import _AdamW, AdamW8bit, AdamW4bit, AdamWFp8
 from .cpu_offload import CPUOffloadOptimizer