Enable CPU/XPU native and ipex path

bitsandbytes/autograd/_functions.py

@@ -8,6 +8,7 @@
 from typing_extensions import deprecated
 
 import bitsandbytes.functional as F
+from bitsandbytes.functional import ipex_cpu, ipex_xpu
 
 # The inverse transformation for the colTuring and colAmpere format were contributed by Alex Borzunov:
 # https://github.com/bigscience-workshop/petals/blob/main/src/petals/utils/linear8bitlt_patch.py
@@ -298,6 +299,64 @@ def backward(ctx: torch.autograd.function.FunctionCtx, grad_output: torch.Tensor
         return grad_A, grad_B, None, grad_bias, None
 
 
+class MatMul8bitFp(torch.autograd.Function):
+    # For Intel CPU and XPU, the double quant has many unsafe operations which will breaks the finetune.
+    # Moreover, the MatMul8bitLt is much slower than MatMul8bitFp in finetune.
+    # The MatMul8bitLt has more mechanisms in computing grad.
+    # We don't have fast kernel for quant/dequant 8bit in CPU/XPU, so it's very slow.
+    # We'd like to use dequant + matmul to run finetune currently.
+
+    @staticmethod
+    def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState):
+        if state.has_fp16_weights or state.CB is None:
+            has_grad = getattr(B, "grad", None) is not None
+            is_transposed = not B.is_contiguous() and B.shape[0] == B.stride(1)
+            if is_transposed:
+                B = B.contiguous()
+
+            if (state.is_training and not has_grad) or state.CB is None or state.SCB is None:
+                state.reset_grads()
+                state.CB, state.SCB, _ = F.int8_vectorwise_quant(B.to(torch.float16))
+                B = state.CB
+
+        CB = state.CB.data.to(A.dtype).mul_(state.SCB.unsqueeze(1).mul(1.0 / 127.0))
+        output = torch.nn.functional.linear(A, CB, bias)
+        # to pass the test: tests/test_modules.py::test_linear8bitlt_no_fp16_weights[2.0-xpu]
+        state.idx = False
+        ctx.state = state
+        ctx.dtype_A = A.dtype
+        ctx.grad_shape = A.shape
+        ctx.A = A
+        ctx.dtype_bias = None if bias is None else bias.dtype
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        req_gradA, req_gradB, _, req_gradBias, _ = ctx.needs_input_grad
+        A = ctx.A
+        state = ctx.state
+        grad_A = grad_B = grad_bias = None
+        if req_gradBias:
+            # compute grad_bias first before changing grad_output dtype
+            grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias)
+
+        # Cast grad_output to fp16
+        if len(grad_output.shape) == 3:
+            grad_output = grad_output.reshape(-1, grad_output.shape[-1]).contiguous()
+
+        if req_gradB:
+            grad_B = torch.matmul(A.t(), grad_output).t()
+
+        if req_gradA:
+            if state.CB is not None:
+                CB = state.CB.to(ctx.dtype_A, copy=True).mul_(state.SCB.unsqueeze(1).mul(1.0 / 127.0))
+                grad_A = torch.matmul(grad_output.to(ctx.dtype_A), CB).view(ctx.grad_shape)
+            else:
+                raise Exception("State must contain CB matrix for backward")
+
+        return grad_A, grad_B, None, grad_bias, None
+
+
 class MatMul4Bit(torch.autograd.Function):
     # forward is the same, but we added the fallback for pre-turing GPUs
     # backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None")
@@ -366,6 +425,10 @@ def matmul(
     state = state or MatmulLtState()
     if threshold > 0.0:
         state.threshold = threshold
+    # MatMul8bitLt is slower because no fast kernel for quant/dequant 8bit in CPU/XPU
+    if state.is_training:
+        if (A.device.type == "cpu" and ipex_cpu) or (A.device.type == "xpu" and ipex_xpu):
+            return MatMul8bitFp.apply(A, B, out, bias, state)
     return MatMul8bitLt.apply(A, B, out, bias, state)
 
 
@@ -378,6 +441,17 @@ def matmul_4bit(
 ):
     assert quant_state is not None
 
+    if A.device.type in ("cpu", "xpu") and A.requires_grad == False:
+        if getattr(quant_state, "ipex", False):
+            # IPEX CPU will change weight to 4D so don't need transpose
+            B = B.t() if B.dim() == 2 else B
+            out = F.gemv_4bit(A, B, out, state=quant_state)
+            if bias is not None:
+                out += bias
+            return out
+        else:
+            return MatMul4Bit.apply(A, B, out, bias, quant_state)
+
     if A.numel() == A.shape[-1] and A.requires_grad == False:
         if A.shape[-1] % quant_state.blocksize != 0:
             warn(

bitsandbytes/backends/cpu/ops.py

@@ -26,22 +26,42 @@ def _(A: torch.Tensor, B: torch.Tensor):
 @register_kernel("bitsandbytes::quantize_blockwise", "cpu")
 def _(A: torch.Tensor, code: torch.Tensor, blocksize: int) -> tuple[torch.Tensor, torch.Tensor]:
     torch._check_is_size(blocksize)
-    torch._check(A.dtype == torch.float32, lambda: f"A must be float32 on cpu, got {A.dtype}")
 
     n = A.numel()
-    blocks = -(n // -blocksize)
-
-    absmax = torch.empty((blocks,), device=A.device, dtype=torch.float32)
-    out = torch.empty_like(A, dtype=torch.uint8)
-
-    lib.cquantize_blockwise_cpu_fp32(
-        get_ptr(code),
-        get_ptr(A),
-        get_ptr(absmax),
-        get_ptr(out),
-        ct.c_longlong(blocksize),
-        ct.c_longlong(n),
-    )
+
+    # Only FP32 has c++ kernrl
+    if A.dtype == torch.float32:
+        blocks = -(n // -blocksize)
+
+        absmax = torch.empty((blocks,), device=A.device, dtype=torch.float32)
+        out = torch.empty_like(A, dtype=torch.uint8)
+
+        lib.cquantize_blockwise_cpu_fp32(
+            get_ptr(code),
+            get_ptr(A),
+            get_ptr(absmax),
+            get_ptr(out),
+            ct.c_longlong(blocksize),
+            ct.c_longlong(n),
+        )
+    else:
+        rem = n % blocksize
+        has_rem = rem > 0
+        blocks = n // blocksize + has_rem
+        absmax = torch.zeros((blocks,), device=A.device, dtype=torch.float32)
+        A_reshaped = A.reshape(n)
+        A_com = A_reshaped[: n - rem]
+        A_com_reshaped = A_com.reshape(n // blocksize, blocksize)
+        absmax[: blocks - has_rem] = torch.abs(A_com_reshaped).max(dim=-1)[0]
+        scaled_A = torch.clamp(A_com_reshaped * (1 / absmax[: blocks - has_rem].view(-1, 1)), -1, 1)
+        scaled_A = scaled_A.reshape(-1)
+        if has_rem:
+            absmax[-1] = torch.abs(A_reshaped[n - rem :]).max()
+            scaled_A_rem = torch.clamp(A_reshaped[n - rem :] * (1 / absmax[-1]), -1, 1)
+            scaled_A = torch.cat([scaled_A, scaled_A_rem], dim=0)
+
+        diff = torch.abs(scaled_A.unsqueeze(-1) - code.to(scaled_A.device))
+        out = torch.argmin(diff, dim=-1).to(torch.uint8).to(scaled_A.device).reshape(A.shape)
 
     return out, absmax
 
@@ -50,18 +70,28 @@ def _(A: torch.Tensor, code: torch.Tensor, blocksize: int) -> tuple[torch.Tensor
 def _(A: torch.Tensor, absmax: torch.Tensor, code: torch.Tensor, blocksize: int, dtype: torch.dtype) -> torch.Tensor:
     torch._check_is_size(blocksize)
     torch._check(A.dtype == torch.uint8, lambda: f"A must be uint8, got {A.dtype}")
-    torch._check(dtype == torch.float32, lambda: f"dtype must be float32 on cpu, got {dtype}")
 
-    out = torch.empty_like(A, dtype=dtype)
-
-    lib.cdequantize_blockwise_cpu_fp32(
-        get_ptr(code),
-        get_ptr(A),
-        get_ptr(absmax),
-        get_ptr(out),
-        ct.c_longlong(blocksize),
-        ct.c_longlong(A.numel()),
-    )
+    # Only FP32 has c++ kernrl
+    if dtype == torch.float32:
+        out = torch.empty_like(A, dtype=dtype)
+
+        lib.cdequantize_blockwise_cpu_fp32(
+            get_ptr(code),
+            get_ptr(A),
+            get_ptr(absmax),
+            get_ptr(out),
+            ct.c_longlong(blocksize),
+            ct.c_longlong(A.numel()),
+        )
+    else:
+        out = code[A.reshape(-1).int()]
+        blocks = out.shape[-1] // blocksize
+        res = out.shape[-1] % blocksize
+        if res != 0:
+            out = torch.nn.functional.pad(out, (0, blocksize - res), mode="constant", value=0)
+        out = (out.view(-1, blocksize) * absmax.view(-1, 1)).to(dtype).reshape(-1)
+        out = out[: blocks * blocksize + res]
+        out = out.reshape(A.shape)
 
     return out
 
@@ -88,31 +118,63 @@ def _(A: torch.Tensor, absmax: torch.Tensor, code: torch.Tensor, blocksize: int,
     dtype=torch.float32,
     device="cpu",
 )
+_FP4_QUANT_TABLE = torch.tensor(
+    [
+        0.0000,
+        0.0052,
+        0.6667,
+        1.0000,
+        0.3333,
+        0.5000,
+        0.1667,
+        0.2500,
+        0.0000,
+        -0.0052,
+        -0.6667,
+        -1.0000,
+        -0.3333,
+        -0.5000,
+        -0.1667,
+        -0.2500,
+    ],
+    dtype=torch.float32,
+    device="cpu",
+)
+CODE = {"nf4": _NF4_QUANT_TABLE, "fp4": _FP4_QUANT_TABLE}
 
 
 @register_kernel("bitsandbytes::quantize_4bit", "cpu")
 def _(
     A: torch.Tensor, blocksize: int, quant_type: str, quant_storage: torch.dtype
 ) -> tuple[torch.Tensor, torch.Tensor]:
     torch._check_is_size(blocksize)
-    torch._check(quant_type == "nf4", lambda: f"quant_type must be nf4 on CPU, got {quant_type}")
+    torch._check(quant_type in ("nf4", "fp4"), lambda: f"quant_type must be nf4 or fp4 on CPU, got {quant_type}")
     torch._check(
         A.dtype in [torch.bfloat16, torch.float16, torch.float32],
         lambda: f"Blockwise 4bit quantization only supports 16/32-bit floats, but got {A.dtype}",
     )
 
     n = A.numel()
-
-    # TODO: Support when weight matrix is not divisible by blocksize
-    torch._check(n % blocksize == 0, lambda: f"n must be divisible by blocksize, got {n} and {blocksize}")
-
-    # Divide into blocks and normalize
-    blocks = A.reshape(-1, blocksize)
-    absmax = blocks.abs().max(dim=1).values.float()
-    scaled = blocks / absmax.unsqueeze(-1)
+    full_blocks = n // blocksize
+    rem = n % blocksize
+    blocks = full_blocks + 1 if rem else full_blocks
+    absmax = torch.zeros((blocks,), device=A.device, dtype=torch.float32)
+    A_flattened = A.reshape(n)
+
+    # Scale full blocks of the tensor to [-1, 1]
+    A_full_blocks = A_flattened[: n - rem].reshape(n // blocksize, blocksize)
+    absmax[:full_blocks] = torch.abs(A_full_blocks).max(dim=-1)[0]
+    scaled = torch.clamp(A_full_blocks * (1 / absmax[:full_blocks].view(-1, 1)), -1, 1).reshape(-1)
+
+    # Scale any partial block
+    if rem:
+        A_rem = A_flattened[-rem:]
+        absmax[-1] = torch.abs(A_rem).max()
+        scaled_rem = torch.clamp(A_rem * (1 / absmax[-1]), -1, 1)
+        scaled = torch.cat([scaled, scaled_rem], dim=0)
 
     # Quantize with the lookup table
-    quantized = torch.argmin(torch.abs(scaled.view(-1, 1) - _NF4_QUANT_TABLE), dim=-1, keepdim=True).to(torch.uint8)
+    quantized = torch.argmin(torch.abs(scaled.view(-1, 1) - CODE[quant_type]), dim=-1, keepdim=True).to(torch.uint8)
 
     # Pack two quantized values per byte
     packed = quantized[::2] << 4 | quantized[1::2]
@@ -133,32 +195,45 @@ def _(
     dtype: torch.dtype,
 ) -> torch.Tensor:
     torch._check_is_size(blocksize)
-    torch._check(quant_type == "nf4", lambda: f"quant_type must be nf4 on CPU, got {quant_type}")
+    torch._check(quant_type in ("nf4", "fp4"), lambda: f"quant_type must be nf4 or fp4 on CPU, got {quant_type}")
     torch._check(
         dtype in [torch.bfloat16, torch.float16, torch.float32],
         lambda: f"Blockwise 4bit dequantization only supports 16/32-bit floats, but got {dtype}",
     )
-    torch._check(
-        A.dtype == torch.uint8,
-        lambda: f"Blockwise 4bit dequantization on CPU only supports uint8 storage, got {A.dtype}",
-    )
-
-    A = A.view(-1, 1)
-
-    # Grab upper and lower nibbles. Using int64 for indexing in the LUT.
-    upper = (A >> 4).to(torch.int64)
-    lower = (A & 0x0F).to(torch.int64)
 
-    # Expand to blocks
-    blocks = torch.cat((upper, lower), dim=1).reshape(-1, blocksize)
+    # Enable non uint8 dtype
+    if A.dtype != torch.uint8:
+        A = A.view(torch.uint8)
+
+    A = A.reshape(-1)
+    # Map nf4 to [-1, 1]
+    out_dq = torch.empty(A.size(0) * 2, dtype=torch.int32, device=A.device)
+    n = out_dq.numel()
+    out_dq[1::2] = A & 0xF
+    out_dq[::2] = A >> 4
+    # code is fp32, cast to dtype to avoid the mismatch issue
+    code = CODE[quant_type].to(dtype)
+    out_dq = code[out_dq]
+
+    # Apply scales
+    if out_dq.numel() != n:
+        assert out_dq.numel() == n + 1
+        out_dq = torch.narrow(out_dq, 0, 0, n)
+    blocks = n // blocksize
+    blocks += 1 if n % blocksize > 0 else 0
+    rem = n % blocksize
+    has_rem = rem > 0
+
+    out = torch.empty(shape, dtype=dtype, device=A.device).reshape(-1)
+    if has_rem:
+        out[: n - rem] = (out_dq[: n - rem].view(-1, blocksize) * absmax[: blocks - has_rem].view(-1, 1)).reshape(-1)
+        out[n - rem :] = out_dq[n - rem :] * absmax[-1]
+    else:
+        out = out_dq.view(-1, blocksize) * absmax.view(-1, 1)
+
+    out = out.reshape(-1, *shape[1:]).to(dtype)
 
-    # Dequantize
-    blocks = _NF4_QUANT_TABLE[blocks] * absmax[:, None]
-
-    # Reshape to original shape
-    blocks = blocks.reshape(-1, *shape[1:])
-
-    return blocks.to(dtype)
+    return out
 
 
 @register_kernel("bitsandbytes::gemv_4bit", "cpu")
@@ -170,17 +245,9 @@ def _(
     code: torch.Tensor,
     blocksize: int,
 ) -> torch.Tensor:
-    # TODO: We need to determine whether `code` is NF4, FP4, or other.
-    # Right now we assume NF4, as this is the only one supported on CPU.
-
-    B_dq = torch.ops.bitsandbytes.dequantize_4bit.default(
-        B,
-        absmax,
-        blocksize,
-        "nf4",
-        shape=shapeB,
-        dtype=A.dtype,
-    )
+    # Applied from dequantize_4bit
+    quant_type = "fp4" if code[1] > 0 else "nf4"
+    B_dq = torch.ops.bitsandbytes.dequantize_4bit.default(B, absmax, blocksize, quant_type, shapeB, A.dtype)
 
     # User called gemv with B.t(), so we need to transpose it back.
     # if B.shape[0] == 1:

bitsandbytes/cextension.py

@@ -283,11 +283,26 @@ def get_native_library() -> BNBNativeLibrary:
     return BNBNativeLibrary(dll)
 
 
+try:
+    # to support Intel CPU/GPU (XPU) backend
+    import intel_extension_for_pytorch as ipex
+
+    ipex_cpu = ipex if ipex._C._has_cpu() else None
+    ipex_xpu = ipex if ipex._C._has_xpu() else None
+except BaseException:
+    ipex_cpu = None
+    ipex_xpu = None
+
+
 try:
     lib = get_native_library()
 except Exception as e:
     error_msg = str(e)
-    logger.error(f"bitsandbytes library load error: {error_msg}\n", exc_info=True)
+    if not (ipex_cpu or ipex_xpu):
+        logger.error(
+            f"bitsandbytes library load error: {error_msg}\n If you are using Intel CPU/XPU, please install intel_extension_for_pytorch to enable required ops",
+            exc_info=True,
+        )
 
     # create a mock with error messaging as fallback
     lib = ErrorHandlerMockBNBNativeLibrary(error_msg)