[Triton/XPU] Support 4bit dequantization logic on Triton (#1629)

* [xpu/triton] Add trtion dequantization kernel

This PR adds xpu backend and trtion kernel for dequantization nf4 dtype.
Trtion is an optional import.
Tests:
	tests/test_functional.py::TestQuantize4BitFunctional supported nf4/fp4 cases
	tests/test_functional.py::Test8BitBlockwiseQuantizeFunctional
implemented quantize_blockwise with binary search that works faster for XPU
        tests/test_linear4bit.py

Signed-off-by: Dmitrii Makarenko <[email protected]>

* align with ipex code

* enable test for ipex

* test_kbit_backprop: skip no longer needed

* remove unused

---------

Signed-off-by: Dmitrii Makarenko <[email protected]>

Author: Devjiu

bitsandbytes/backends/triton/__init__.py

@@ -0,0 +1,166 @@
+from collections.abc import Sequence
+
+import torch
+
+from . import triton_kernels
+
+# currently codes unused, kept for reference
+# Should be the same for quant/dequant
+# from bitsandbytes.functional import get_4bit_type
+# _FP4_QUANT_TABLE = get_4bit_type("fp4", device="xpu")
+# _NF4_QUANT_TABLE = get_4bit_type("nf4", device="xpu")
+
+
+def quantize_blockwise(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 xpu, got {A.dtype}")
+
+    n = A.numel()
+    blocks = -(n // -blocksize)
+
+    absmax = torch.empty((blocks,), device=A.device, dtype=A.dtype)
+    out = torch.empty_like(A.flatten(), dtype=torch.uint8)
+
+    triton_kernels.quantize_blockwise_triton(A, blocksize, code, blocks, absmax, out)
+    out = out.reshape(A.shape)
+
+    return out, absmax.float()
+
+
+def dequantize_blockwise(
+    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 xpu, got {dtype}")
+
+    out = torch.empty_like(A, dtype=dtype, device=A.device)
+    triton_kernels.dequant_int8_blockwise(
+        A,
+        code,
+        absmax,
+        out,
+        blocksize,
+    )
+
+    return out
+
+
+def dequantize_blockwise_inplace(
+    A: torch.Tensor, absmax: torch.Tensor, code: torch.Tensor, blocksize: int, dtype: torch.dtype, out: torch.Tensor
+) -> None:
+    torch._check_is_size(blocksize)
+    torch._check(A.dtype == torch.uint8, lambda: f"A must be uint8, got {A.dtype}")
+    torch._check(out.shape == A.shape, lambda: f"Expected out.shape == {A.shape}, got {out.shape}")
+    torch._check(out.device == A.device, lambda: f"Expected out.device == {A.device}, got {out.device}")
+    torch._check(out.dtype == dtype, lambda: f"Expected out.dtype == {dtype}, got {out.dtype}")
+
+    triton_kernels.dequant_int8_blockwise(
+        A,
+        code,
+        absmax,
+        out,
+        blocksize,
+    )
+
+
+def quantize_4bit(
+    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(
+        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}")
+
+    blocks = -(n // -(blocksize * 2))
+
+    absmax = torch.empty((blocks * 2,), device=A.device, dtype=A.dtype)
+    out = torch.empty((n // 2, 1), device=A.device, dtype=torch.uint8)
+
+    triton_kernels.quantize_4bit_blockwise_triton(
+        A, blocksize, quant_type, blocks, absmax, num_elements=n, quantized_out=out
+    )
+    packed = out
+
+    if quant_storage != torch.uint8:
+        packed = out.squeeze().view(quant_storage).unsqueeze(1)
+
+    return packed, absmax.float()
+
+
+def dequantize_4bit(
+    A: torch.Tensor,
+    absmax: torch.Tensor,
+    blocksize: int,
+    quant_type: str,
+    shape: Sequence[int],
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    torch._check_is_size(blocksize)
+    # torch._check(quant_type == "nf4", lambda: f"quant_type must be nf4 on XPU, 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 XPU only supports uint8 storage, got {A.dtype}",
+    # )
+    # Check if this is fine and fast
+    if A.dtype != torch.uint8:
+        A = A.squeeze().view(torch.uint8).unsqueeze(1)
+
+    out = torch.empty(shape, dtype=dtype, device=A.device)
+
+    triton_kernels._dequantize_4bit_impl(A, absmax, blocksize, quant_type, dtype, out=out)
+    return out
+
+
+def dequantize_4bit_inplace(
+    A: torch.Tensor,
+    absmax: torch.Tensor,
+    blocksize: int,
+    quant_type: str,
+    shape: Sequence[int],
+    dtype: torch.dtype,
+    out: torch.Tensor,
+) -> None:
+    torch._check(out.shape == shape, lambda: f"Expected out.shape == {shape}, got {out.shape}")
+    torch._check(out.dtype == dtype, lambda: f"Expected out.dtype == {dtype}, got {out.dtype}")
+    triton_kernels._dequantize_4bit_impl(A, absmax, blocksize, quant_type, dtype, out=out)
+
+
+def gemv_4bit(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    shapeB: Sequence[int],
+    absmax: torch.Tensor,
+    code: torch.Tensor,
+    blocksize: int,
+) -> torch.Tensor:
+    if B.dtype != torch.uint8:
+        B = B.squeeze().view(torch.uint8).unsqueeze(1)
+
+    B_dq_triton = torch.empty(shapeB, dtype=A.dtype, device=A.device)
+
+    triton_kernels._dequantize_4bit_impl_passing_code(
+        B,
+        absmax,
+        blocksize,
+        code,
+        dtype=A.dtype,
+        out=B_dq_triton,
+    )
+
+    return torch.nn.functional.linear(
+        A,
+        B_dq_triton,
+        bias=None,
+    )