BitNet b1.58 training

benchmarks/quantized_training/pretrain_llama2.py

@@ -1,9 +1,10 @@
 # pre-train a mini Llama2 on TinyStories with INT8 quantized training
 # pip install huggingface_hub 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
-# INT8 MP:       python benchmarks/quantized_training/pretrain_llama2.py --seed 2024 --n_steps 10_000 --compile --quantize int8_mixed_precision
+# BF16 baseline: python benchmarks/quantized_training/pretrain_llama2.py --seed 2024 --bf16_model --compile
+# INT8 QT:       python benchmarks/quantized_training/pretrain_llama2.py --seed 2024 --bf16_model --compile --quantize int8_weight_only
+# INT8 MP:       python benchmarks/quantized_training/pretrain_llama2.py --seed 2024 --bf16_model --compile --quantize int8_mixed_precision
+# BitNet:        python benchmarks/quantized_training/pretrain_llama2.py --seed 2024 --bf16_model --compile --quantize bitnet
 
 import os
 
@@ -20,14 +21,14 @@
 from torch.utils.checkpoint import checkpoint
 from tqdm import tqdm
 
-from torchao._models.llama.model import ModelArgs, Transformer, transformer_configs
+from torchao import quantize_
+from torchao._models.llama.model import ModelArgs, Transformer, transformer_configs, RMSNorm
 from torchao.prototype import low_bit_optim
 from torchao.prototype.quantized_training import (
+    bitnet_training,
     int8_mixed_precision_training,
     int8_weight_only_quantized_training,
 )
-from torchao.quantization.quant_api import quantize_
-
 
 # not official models
 transformer_configs.update(
@@ -92,6 +93,8 @@ def get_tinystories():
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--model", default="470M", choices=transformer_configs.keys())
+    parser.add_argument("--bf16_model", action="store_true")
+    parser.add_argument("--bf16_amp", action="store_true")
     parser.add_argument("--quantize")
     parser.add_argument("--activation_checkpointing", action="store_true")
     parser.add_argument("--compile", action="store_true")
@@ -104,7 +107,7 @@ def get_tinystories():
     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("--project", default="quantized_training")
     parser.add_argument("--run_name")
     parser.add_argument("--seed", type=int)
     parser.add_argument("--log_interval", type=int, default=10)
@@ -115,7 +118,10 @@ def get_tinystories():
 
     config = ModelArgs.from_name(args.model)
     config.block_size = args.seq_len
-    model = Transformer(config).bfloat16().cuda()
+    model = Transformer(config)
+    if args.bf16_model:
+        model.bfloat16()
+    model.cuda()
     with torch.device("cuda"):
         model.setup_caches(args.batch_size, args.seq_len, training=True)
     if args.activation_checkpointing:
@@ -126,8 +132,30 @@ def get_tinystories():
     # TODO: might want to do the same for int8_weight_only to standardize.
     if args.quantize == "int8_weight_only":
         quantize_(model, int8_weight_only_quantized_training(), set_inductor_config=False)
+
     elif args.quantize == "int8_mixed_precision":
         quantize_(model.layers, int8_mixed_precision_training(), set_inductor_config=False)
+
+    elif args.quantize == "bitnet":
+        quantize_(model.layers, bitnet_training(), set_inductor_config=False)
+
+        # remove old RMSNorm
+        for layer in model.layers:
+            layer.attention_norm = torch.nn.Identity()
+            layer.ffn_norm = torch.nn.Identity()
+
+        # insert new RMSNorm
+        def insert_rmsnorm(module: torch.nn.Module):
+            for name, child in module.named_children():
+                if isinstance(child, torch.nn.Linear):
+                    w = child.weight
+                    norm = RMSNorm(child.in_features).to(device=w.device, dtype=w.dtype)
+                    setattr(module, name, torch.nn.Sequential(norm, child))
+                else:
+                    insert_rmsnorm(child)
+
+        insert_rmsnorm(model.layers)
+
     elif args.quantize is not None:
         raise ValueError(f"Unsupported quantize={args.quantize}")
 
@@ -155,7 +183,8 @@ def get_tinystories():
         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)
+        with torch.autocast("cuda", torch.bfloat16, enabled=args.bf16_amp):
+            loss = _get_loss(model, batch)
         loss.backward()
 
         if step % args.log_interval == 0:
@@ -165,10 +194,6 @@ def get_tinystories():
                 max_memory_allocated=torch.cuda.max_memory_allocated() / 1e9,
                 max_memory_reserved=torch.cuda.max_memory_reserved() / 1e9,
             )
-            if step > 0:
-                time1 = time.time()
-                log_dict["tokens_per_second"] = (args.log_interval * args.batch_size * args.seq_len) / (time1 - time0)
-                time0 = time1
             run.log(log_dict, step=step)
             pbar.set_postfix(loss=log_dict["loss"])
 
@@ -178,4 +203,10 @@ def get_tinystories():
         step += 1
         pbar.update()
 
+        if step % args.log_interval == 0:
+            time1 = time.time()
+            log_dict = dict(tokens_per_second=(args.log_interval * args.batch_size * args.seq_len) / (time1 - time0))
+            time0 = time1
+            run.log(log_dict, step=step)
+
     run.finish()

test/prototype/test_quantized_training.py

@@ -1,6 +1,6 @@
 import pytest
 
-from torchao.utils import TORCH_VERSION_AT_LEAST_2_4
+from torchao.utils import TORCH_VERSION_AT_LEAST_2_4, TORCH_VERSION_AT_LEAST_2_6
 
 if not TORCH_VERSION_AT_LEAST_2_4:
     pytest.skip("Requires torch>=2.4", allow_module_level=True)
@@ -11,7 +11,7 @@
 import torch.distributed as dist
 import torch.nn.functional as F
 from torch import nn
-from torch.distributed._composable.fsdp import fully_shard, MixedPrecisionPolicy
+from torch.distributed._composable.fsdp import MixedPrecisionPolicy, fully_shard
 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
@@ -20,6 +20,7 @@
 from torchao.prototype.low_bit_optim import _AdamW
 from torchao.prototype.quantized_training import (
     Int8MixedPrecisionTrainingConfig,
+    bitnet_training,
     int8_mixed_precision_training,
     int8_weight_only_quantized_training,
     quantize_int8_rowwise,
@@ -187,6 +188,70 @@ def snr(ref, actual):
         assert snr(inputs_ref.grad, inputs_int8mp.grad) > 20
         assert snr(linear.weight.grad, linear_int8mp.weight.grad) > 20
 
+    @parametrize("compile", [False, True])
+    @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+    def test_bitnet_training(self, compile):
+        # reference implementation
+        # https://github.com/microsoft/unilm/blob/master/bitnet/The-Era-of-1-bit-LLMs__Training_Tips_Code_FAQ.pdf
+        # Figure 3
+        class BitLinear(nn.Linear):
+            def activation_quant(self, x):
+                scale = 127.0 / x.abs().max(dim=-1, keepdim=True).values.clamp_(min=1e-5)
+                return (x * scale).round().clamp_(-128, 127) / scale
+
+            def weight_quant(self, x):
+                scale = 1.0 / x.abs().mean().clamp_(min=1e-5)
+                return (x * scale).round().clamp_(-1, 1) / scale
+
+            def forward(self, x):
+                w = self.weight
+                x = x + (self.activation_quant(x) - x).detach()
+                w = w + (self.weight_quant(w) - w).detach()
+                return F.linear(x, w, self.bias)
+
+        _reset()
+        bsize = 4
+        embed_dim = 32
+        device = "cuda"
+
+        # only use 1 matmul shape to reduce triton autotune time
+        model_ref = nn.Sequential(
+            nn.Linear(embed_dim, embed_dim, bias=False),
+            nn.GELU(),
+            nn.Linear(embed_dim, embed_dim),
+        ).to(device)
+        model = copy.deepcopy(model_ref)
+        quantize_(model, bitnet_training(), set_inductor_config=False)
+
+        # change model_ref to use BitLinear
+        model_ref[0].__class__ = BitLinear
+        model_ref[2].__class__ = BitLinear
+
+        if compile:
+            model_ref.compile()
+            model.compile()
+
+        optim_ref = torch.optim.AdamW(model_ref.parameters())
+        optim = torch.optim.AdamW(model.parameters())
+
+        for i in range(5):
+            inputs = torch.randn(bsize, embed_dim, device=device)
+            labels = torch.randint(embed_dim, size=(bsize,), device=device)
+            loss_ref = F.cross_entropy(model_ref(inputs), labels)
+            loss = F.cross_entropy(model(inputs), labels)
+
+            torch.testing.assert_close(loss, loss_ref)
+
+            loss_ref.backward()
+            optim_ref.step()
+            optim_ref.zero_grad()
+
+            loss.backward()
+            for p in model.parameters():
+                assert p.grad is not None
+            optim.step()
+            optim.zero_grad()
+
 
 _FSDP_WORLD_SIZE = 2
 
@@ -198,35 +263,36 @@ def world_size(self) -> int:
 
     @skip_if_lt_x_gpu(_FSDP_WORLD_SIZE)
     def test_fsdp2_correctness(self):
+        mp_policy = MixedPrecisionPolicy()
+
+        # quantize_fn, mp_policy, tolerance
         test_args = [
-            (
-                int8_weight_only_quantized_training(),  # quantize_fn for base model
-                int8_weight_only_quantized_training(),  # quantize_fn for FSDP model
-                MixedPrecisionPolicy(),
-                0.05,  # tolerance. due to stochastic rounding, use a pretty large tolerance here
-            ),
-            (
-                int8_mixed_precision_training(),
-                int8_mixed_precision_training(),
-                MixedPrecisionPolicy(),
-                1e-6,
-            ),
-            (
-                # It's complicated (though possible) to simulate FSDP BF16 mixed-precision for base_model.
-                # We would need to cast all params to BF16 in forward and backward pass, while keeping
-                # the params in FP32 for optim step.
-                # torch.autocast() will only do this for F.linear() layer (and its backward).
-                # To keep it simple, we just use a larger tolerance here.
-                int8_mixed_precision_training(),
-                int8_mixed_precision_training(Int8MixedPrecisionTrainingConfig(fsdp_param_dtype=torch.bfloat16)),
-                MixedPrecisionPolicy(param_dtype=torch.bfloat16),
-                1e-2,
-            ),
+            # high tolerance due to stochastic rounding
+            (int8_weight_only_quantized_training, mp_policy, 0.05),
+            (int8_mixed_precision_training, mp_policy, 1e-6),
+            (bitnet_training, mp_policy, 1e-5),
         ]
+
+        # FSDP2 mixed-precision requires https://github.com/pytorch/pytorch/pull/136129
+        if TORCH_VERSION_AT_LEAST_2_6:
+            # It's complicated (though possible) to simulate FSDP BF16 mixed-precision for base_model.
+            # We would need to cast all params to BF16 in forward and backward pass, while keeping
+            # the params in FP32 for optim step.
+            # torch.autocast() will only do this for F.linear() layer (and its backward).
+            # To keep it simple, we just use a larger tolerance here.
+            bf16_mp_policy = MixedPrecisionPolicy(param_dtype=torch.bfloat16)
+
+            extra_args = [
+                (int8_weight_only_quantized_training, bf16_mp_policy, 1e-2),
+                (int8_mixed_precision_training, bf16_mp_policy, 1e-2),
+                (bitnet_training, bf16_mp_policy, 1e-2),
+            ]
+            test_args.extend(extra_args)
+
         self.run_subtests({"args": test_args}, self._run_subtest)
 
     def _run_subtest(self, args):
-        base_quantize_fn, fsdp_quantize_fn, mp_policy, tolerance = args
+        quantize_fn, mp_policy, tolerance = args
 
         batch_size = 3
         vocab_size = 32
@@ -245,8 +311,8 @@ def _run_subtest(self, args):
         base_model = Transformer(model_args).cuda()
         fsdp_model = copy.deepcopy(base_model)
 
-        quantize_(base_model.layers, base_quantize_fn, set_inductor_config=False)
-        quantize_(fsdp_model.layers, fsdp_quantize_fn, set_inductor_config=False)
+        quantize_(base_model.layers, quantize_fn(), set_inductor_config=False)
+        quantize_(fsdp_model.layers, quantize_fn(), set_inductor_config=False)
 
         for layer in fsdp_model.layers:
             fully_shard(layer, mp_policy=mp_policy)
@@ -275,7 +341,25 @@ def _run_subtest(self, args):
             base_optim.step()
 
             rel_error = (fsdp_loss - base_loss).abs() / base_loss.abs()
-            assert rel_error < tolerance, (iter_idx, rel_error)
+            assert rel_error < tolerance, (quantize_fn.__name__, mp_policy, iter_idx, rel_error)
+
+    @skip_if_lt_x_gpu(_FSDP_WORLD_SIZE)
+    def test_precompute_bitnet_scale(self):
+        from torchao.prototype.quantized_training.bitnet import get_bitnet_scale, precompute_bitnet_scale_for_fsdp
+
+        model = nn.Sequential(nn.Linear(32, 64), nn.GELU(), nn.Linear(64, 32)).cuda()
+        model_fsdp = copy.deepcopy(model)
+        quantize_(model_fsdp, bitnet_training())
+        fully_shard(model_fsdp)
+
+        precompute_bitnet_scale_for_fsdp(model_fsdp)
+
+        torch.testing.assert_close(
+            get_bitnet_scale(model[0].weight), model_fsdp[0].weight._local_tensor._precomputed_scale
+        )
+        torch.testing.assert_close(
+            get_bitnet_scale(model[2].weight), model_fsdp[2].weight._local_tensor._precomputed_scale
+        )
 
 
 instantiate_parametrized_tests(TestQuantizedTraining)