Add kernel registration for 8bit and 32bit optimizers

bitsandbytes/_ops.py

@@ -348,3 +348,107 @@ def _(
     ) -> torch.Tensor:
         torch._check_is_size(blocksize)
         return torch.empty(shape, dtype=dtype, device=A.device)
+
+
+torch.library.define(
+    "bitsandbytes::optimizer_update_32bit",
+    "(str optimizer_name, Tensor(a0!) g, Tensor(a1!) p, Tensor(a2!) state1, Tensor(a3!)? state2, Tensor(a4!)? unorm_vec, float max_unorm, float param_norm, float beta1, float beta2, float beta3, float alpha, float eps, float weight_decay, int step, float lr, float gnorm_scale, bool skip_zeros=False) -> ()",
+)
+
+
+@register_fake("bitsandbytes::optimizer_update_32bit")
+def _(
+    optimizer_name: str,
+    g: torch.Tensor,
+    p: torch.Tensor,
+    state1: torch.Tensor,
+    state2: Optional[torch.Tensor],
+    unorm_vec: Optional[torch.Tensor],
+    max_unorm: float,
+    param_norm: float,
+    beta1: float,
+    beta2: float,
+    beta3: float,
+    alpha: float,
+    eps: float,
+    weight_decay: float,
+    step: int,
+    lr: float,
+    gnorm_scale: float,
+    skip_zeros=False,
+) -> None:
+    torch._check(
+        g.numel() == p.numel(),
+        lambda: f"g and p must have the same number of elements, got {g.numel()} and {p.numel()}",
+    )
+    compute_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+
+    torch._check(
+        g.dtype in compute_dtypes,
+        lambda: f"g must be bfloat16, float16, or float32, got {g.dtype}",
+    )
+    torch._check(
+        g.dtype == p.dtype,
+        lambda: f"Expected all tensors to have the same dtype, got g.dtype={g.dtype}, p.dtype={p.dtype}",
+    )
+
+
+torch.library.define(
+    "bitsandbytes::optimizer_update_8bit_blockwise",
+    "(str optimizer_name, Tensor(a0!) g, Tensor(a1!) p, Tensor(a2!) state1, Tensor(a3!)? state2, float beta1, float beta2, float beta3, float alpha, float eps, int step, float lr, Tensor(a4!) qmap1, Tensor(a5!)? qmap2, Tensor(a6!) absmax1, Tensor(a7!)? absmax2, float weight_decay, float gnorm_scale, bool skip_zeros=False) -> ()",
+)
+
+
+@register_fake("bitsandbytes::optimizer_update_8bit_blockwise")
+def _(
+    optimizer_name: str,
+    g: torch.Tensor,
+    p: torch.Tensor,
+    state1: torch.Tensor,
+    state2: Optional[torch.Tensor],
+    beta1: float,
+    beta2: float,
+    beta3: float,
+    alpha: float,
+    eps: float,
+    step: int,
+    lr: float,
+    qmap1: torch.Tensor,
+    qmap2: Optional[torch.Tensor],
+    absmax1: torch.Tensor,
+    absmax2: Optional[torch.Tensor],
+    weight_decay: float,
+    gnorm_scale: float,
+    skip_zeros=False,
+) -> None:
+    torch._check(
+        g.numel() == p.numel(),
+        lambda: f"g and p must have the same number of elements, got {g.numel()} and {p.numel()}",
+    )
+    compute_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+
+    torch._check(
+        g.dtype in compute_dtypes,
+        lambda: f"g must be bfloat16, float16, or float32, got {g.dtype}",
+    )
+    torch._check(
+        g.dtype == p.dtype,
+        lambda: f"Expected all tensors to have the same dtype, got g.dtype={g.dtype}, p.dtype={p.dtype}",
+    )
+    torch._check(
+        state1.dtype == torch.uint8,
+        lambda: f"state1 must be uint8, got {state1.dtype}",
+    )
+    torch._check(
+        qmap1.dtype == absmax1.dtype == torch.float32,
+        lambda: f"Expected qmap1 and absmax1 to be float32, got qmap1.dtype={qmap1.dtype}, absmax1.dtype={absmax1.dtype}",
+    )
+    if state2 is not None:
+        torch._check(
+            state2.dtype == torch.uint8,
+            lambda: f"state2 must be uint8, got {state2.dtype}",
+        )
+        torch._check(
+            qmap2.dtype == absmax2.dtype == torch.float32,
+            lambda: f"Expected qmap2 and absmax2 to be float32, got qmap2.dtype={qmap2.dtype}, absmax2.dtype={absmax2.dtype}",
+        )

bitsandbytes/backends/cuda/ops.py

@@ -538,3 +538,229 @@ def _gemv_4bit_impl(
                 ct.c_int32(blocksize),
                 stream,
             )
+
+
+"""C FUNCTIONS FOR OPTIMIZERS"""
+str2optimizer32bit = {
+    "adam": (
+        lib.cadam32bit_grad_fp32,
+        lib.cadam32bit_grad_fp16,
+        lib.cadam32bit_grad_bf16,
+    ),
+    "momentum": (
+        lib.cmomentum32bit_grad_32,
+        lib.cmomentum32bit_grad_16,
+    ),
+    "rmsprop": (
+        lib.crmsprop32bit_grad_32,
+        lib.crmsprop32bit_grad_16,
+    ),
+    "lion": (
+        lib.clion32bit_grad_fp32,
+        lib.clion32bit_grad_fp16,
+        lib.clion32bit_grad_bf16,
+    ),
+    "adagrad": (
+        lib.cadagrad32bit_grad_32,
+        lib.cadagrad32bit_grad_16,
+    ),
+    "lamb": (
+        lib.cadam32bit_grad_fp32,
+        lib.cadam32bit_grad_fp16,
+        lib.cadam32bit_grad_bf16,
+    ),
+    "ademamix": (
+        lib.cademamix32bit_grad_fp32,
+        lib.cademamix32bit_grad_fp16,
+        lib.cademamix32bit_grad_bf16,
+    ),
+}
+
+str2optimizer8bit_blockwise = {
+    "adam": (
+        lib.cadam_8bit_blockwise_grad_fp32,
+        lib.cadam_8bit_blockwise_grad_fp16,
+        lib.cadam_8bit_blockwise_grad_bf16,
+    ),
+    "momentum": (
+        lib.cmomentum_8bit_blockwise_grad_fp32,
+        lib.cmomentum_8bit_blockwise_grad_fp16,
+        lib.cmomentum_8bit_blockwise_grad_bf16,
+    ),
+    "rmsprop": (
+        lib.crmsprop_8bit_blockwise_grad_fp32,
+        lib.crmsprop_8bit_blockwise_grad_fp16,
+        lib.crmsprop_8bit_blockwise_grad_bf16,
+    ),
+    "lion": (
+        lib.clion_8bit_blockwise_grad_fp32,
+        lib.clion_8bit_blockwise_grad_fp16,
+        lib.clion_8bit_blockwise_grad_bf16,
+    ),
+    "adagrad": (
+        lib.cadagrad_8bit_blockwise_grad_fp32,
+        lib.cadagrad_8bit_blockwise_grad_fp16,
+        lib.cadagrad_8bit_blockwise_grad_bf16,
+    ),
+    "ademamix": (
+        lib.cademamix_8bit_blockwise_grad_fp32,
+        lib.cademamix_8bit_blockwise_grad_fp16,
+        lib.cademamix_8bit_blockwise_grad_bf16,
+    ),
+}
+
+
+def _optimizer_update_32bit_impl(
+    optimizer_name: str,
+    g: torch.Tensor,
+    p: torch.Tensor,
+    state1: torch.Tensor,
+    state2: Optional[torch.Tensor],
+    unorm_vec: Optional[torch.Tensor],
+    max_unorm: float,
+    param_norm: float,
+    beta1: float,
+    beta2: float,
+    beta3: float,
+    alpha: float,
+    eps: float,
+    weight_decay: float,
+    step: int,
+    lr: float,
+    gnorm_scale: float,
+    skip_zeros=False,
+) -> None:
+    optim_fns = str2optimizer32bit.get(optimizer_name, None)
+    if optim_fns is None:
+        raise ValueError(
+            f"Unsupported optimizer name: {optimizer_name}. Supported optimizers: {list(str2optimizer8bit_blockwise.keys())}"
+        )
+    if g.dtype == torch.float32:
+        optim_func = optim_fns[0]
+    elif g.dtype == torch.float16:
+        optim_func = optim_fns[1]
+    elif g.dtype == torch.bfloat16 and len(optim_fns) == 3:
+        optim_func = optim_fns[2]
+    else:
+        raise ValueError(
+            f"Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}",
+        )
+
+    with _cuda_device_of(g):
+        optim_func(
+            get_ptr(g),
+            get_ptr(p),
+            get_ptr(state1),
+            get_ptr(state2),
+            get_ptr(unorm_vec),
+            ct.c_float(max_unorm),
+            ct.c_float(param_norm),
+            ct.c_float(beta1),
+            ct.c_float(beta2),
+            ct.c_float(beta3),
+            ct.c_float(alpha),
+            ct.c_float(eps),
+            ct.c_float(weight_decay),
+            ct.c_int32(step),
+            ct.c_float(lr),
+            ct.c_float(gnorm_scale),
+            ct.c_bool(skip_zeros),
+            ct.c_int32(g.numel()),
+        )
+
+
+def _optimizer_update_8bit_blockwise_impl(
+    optimizer_name: str,
+    g: torch.Tensor,
+    p: torch.Tensor,
+    state1: torch.Tensor,
+    state2: Optional[torch.Tensor],
+    beta1: float,
+    beta2: float,
+    beta3: float,
+    alpha: float,
+    eps: float,
+    step: int,
+    lr: float,
+    qmap1: torch.Tensor,
+    qmap2: Optional[torch.Tensor],
+    absmax1: torch.Tensor,
+    absmax2: Optional[torch.Tensor],
+    weight_decay: float,
+    gnorm_scale: float,
+    skip_zeros=False,
+) -> None:
+    # torch._check(
+    #     g.numel() == p.numel(),
+    #     lambda: f"g and p must have the same number of elements, got {g.numel()} and {p.numel()}",
+    # )
+    # compute_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+
+    # torch._check(
+    #     g.dtype in compute_dtypes,
+    #     lambda: f"g must be bfloat16, float16, or float32, got {g.dtype}",
+    # )
+    # torch._check(
+    #     g.dtype == p.dtype,
+    #     lambda: f"Expected all tensors to have the same dtype, got g.dtype={g.dtype}, p.dtype={p.dtype}",
+    # )
+    # torch._check(
+    #     state1.dtype == torch.uint8,
+    #     lambda: f"state1 must be uint8, got {state1.dtype}",
+    # )
+    # torch._check(
+    #     qmap1.dtype == absmax1.dtype == torch.float32,
+    #     lambda: f"Expected qmap1 and absmax1 to be float32, got qmap1.dtype={qmap1.dtype}, absmax1.dtype={absmax1.dtype}",
+    # )
+    # if state2 is not None:
+    #     torch._check(
+    #         state2.dtype == torch.uint8,
+    #         lambda: f"state2 must be uint8, got {state2.dtype}",
+    #     )
+    #     torch._check(
+    #         qmap2.dtype == absmax2.dtype == torch.float32,
+    #         lambda: f"Expected qmap2 and absmax2 to be float32, got qmap2.dtype={qmap2.dtype}, absmax2.dtype={absmax2.dtype}",
+    #     )
+    optimizer_fns = str2optimizer8bit_blockwise.get(optimizer_name)
+    if optimizer_fns is None:
+        raise ValueError(
+            f"Unsupported optimizer name: {optimizer_name}. Supported optimizers: {list(str2optimizer8bit_blockwise.keys())}"
+        )
+
+    if g.dtype == torch.float32:
+        optimizer_fn = optimizer_fns[0]
+    elif g.dtype == torch.float16:
+        optimizer_fn = optimizer_fns[1]
+    elif g.dtype == torch.bfloat16:
+        optimizer_fn = optimizer_fns[2]
+    else:
+        raise ValueError(
+            f"Unsupported gradient dtype: {g.dtype}. Supported dtypes: torch.float32, torch.float16, torch.bfloat16"
+        )
+
+    with _cuda_device_of(g):
+        optimizer_fn(
+            get_ptr(p),
+            get_ptr(g),
+            get_ptr(state1),
+            get_ptr(state2),
+            ct.c_float(beta1),
+            ct.c_float(beta2),
+            ct.c_float(beta3),
+            ct.c_float(alpha),
+            ct.c_float(eps),
+            ct.c_int32(step),
+            ct.c_float(lr),
+            get_ptr(qmap1),
+            get_ptr(qmap2),
+            get_ptr(absmax1),
+            get_ptr(absmax2),
+            ct.c_float(weight_decay),
+            ct.c_float(gnorm_scale),
+            ct.c_bool(skip_zeros),
+            ct.c_int32(g.numel()),
+        )
+
+
+register_kernel("bitsandbytes::optimizer_update_8bit_blockwise", "cuda")(_optimizer_update_8bit_blockwise_impl)
+register_kernel("bitsandbytes::optimizer_update_32bit", "cuda")(_optimizer_update_32bit_impl)