Added missing triton and fp8 files.

bitsandbytes/research/__init__.py

@@ -0,0 +1,7 @@
+
+from .autograd._functions import (
+    matmul_fp8,
+    switchback_bnb,
+    matmul_fp8_global,
+    matmul_fp8_mixed,
+)

bitsandbytes/research/autograd/_functions.py

@@ -0,0 +1,493 @@
+import operator
+import warnings
+from dataclasses import dataclass
+from functools import reduce  # Required in Python 3
+
+import torch
+
+import bitsandbytes.functional as F
+
+from bitsandbytes.autograd._functions import MatmulLtState, GlobalOutlierPooler
+
+
+# math.prod not compatible with python < 3.8
+def prod(iterable):
+    return reduce(operator.mul, iterable, 1)
+
+tensor = torch.Tensor
+
+class MatMulFP8(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")
+
+    @staticmethod
+    def forward(ctx, A, B, out=None, fw_code=None, bw_code=None, bsz=1024, bsz2=1024):
+        # default of pytorch behavior if inputs are empty
+        ctx.is_empty = False
+        if prod(A.shape) == 0:
+            ctx.is_empty = True
+            ctx.A = A
+            ctx.B = B
+
+            B_shape = B.shape
+            if A.shape[-1] == B_shape[0]:
+                return torch.empty(A.shape[:-1] + B_shape[1:], dtype=A.dtype, device=A.device)
+            else:
+                return torch.empty(A.shape[:-1] + B_shape[:1], dtype=A.dtype, device=A.device)
+
+        # 1. Dequantize
+        # 2. MatmulnN
+        cA, state = F.quantize_blockwise(A, code=fw_code, blocksize=bsz)
+        fp8A = F.dequantize_blockwise(cA, state, blocksize=bsz).to(A.dtype)
+
+        cB, state = F.quantize(B.float(), code=fw_code)
+        fp8B = F.dequantize(cB, state).to(B.dtype)
+
+        output = torch.matmul(fp8A, fp8B)
+
+        # output is half
+
+        # 3. Save state
+        ctx.fw_code = fw_code
+        ctx.bw_code = bw_code
+        ctx.bsz = bsz
+        ctx.bsz2 = bsz2
+        ctx.dtype_A, ctx.dtype_B = A.dtype, B.dtype
+
+        if any(ctx.needs_input_grad[:2]):
+            # NOTE: we send back A, and re-quant.
+            ctx.tensors = (A, fp8B)
+        else:
+            ctx.tensors = (None, None)
+
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if ctx.is_empty:
+            return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, None, None, None, None
+
+        req_gradA, req_gradB, _, _, _, _, _ = ctx.needs_input_grad
+        A, B = ctx.tensors
+
+        grad_A, grad_B = None, None
+
+        cgrad_out, state = F.quantize_blockwise(grad_output, code=ctx.bw_code, blocksize=ctx.bsz2)
+        fp8out = F.dequantize_blockwise(cgrad_out, state, blocksize=ctx.bsz2).to(grad_output.dtype)
+
+        cgrad_output_2, state_2 = F.quantize(grad_output.float(), code=ctx.bw_code)
+        fp8out_2 = F.dequantize(cgrad_output_2, state_2).to(grad_output.dtype)
+
+        # grad_output_reshape = grad_output.reshape(-1, grad_output.shape[-1]).contiguous()
+        # fp8grad_transpose, stategrad_transpose = F.vectorwise_quant(grad_output_reshape, dim=0, quant_type='vector')
+        # fp8out_transpose = (fp8grad_transpose / 7) * stategrad_transpose
+        # fp8out_transpose = fp8out_transpose.view(grad_output.shape[0], grad_output.shape[1], grad_output.shape[2])
+
+        # not supported by PyTorch. TODO: create work-around
+        if req_gradA: 
+            grad_A = torch.matmul(fp8out, B.t().to(fp8out.dtype)).to(A.dtype)
+
+        if req_gradB:
+            if len(A.shape) == 3:
+                At = A.transpose(2, 1).contiguous()
+            else:
+                At = A.transpose(1, 0).contiguous()
+            cA, state = F.quantize(At.float(), code=ctx.fw_code)
+            fp8At = F.dequantize(cA, state).to(A.dtype)
+            grad_B = torch.matmul(fp8At.to(fp8out_2.dtype), fp8out_2).to(B.dtype)
+
+        return grad_A, grad_B, None, None, None, None, None
+    
+class MatMulFP8Mixed(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")
+
+    @staticmethod
+    def forward(ctx, A, B, out=None, fw_code=None, bw_code=None, bsz=1024, bsz2=1024):
+        # default of pytorch behavior if inputs are empty
+        ctx.is_empty = False
+        if prod(A.shape) == 0:
+            ctx.is_empty = True
+            ctx.A = A
+            ctx.B = B
+
+            B_shape = B.shape
+            if A.shape[-1] == B_shape[0]:
+                return torch.empty(A.shape[:-1] + B_shape[1:], dtype=A.dtype, device=A.device)
+            else:
+                return torch.empty(A.shape[:-1] + B_shape[:1], dtype=A.dtype, device=A.device)
+
+        # 1. Dequantize
+        # 2. MatmulnN
+        cA, state = F.quantize_blockwise(A, code=fw_code, blocksize=bsz)
+        fp8A = F.dequantize_blockwise(cA, state, blocksize=bsz).to(A.dtype)
+
+        cB, state = F.quantize(B.float(), code=fw_code)
+        fp8B = F.dequantize(cB, state).to(B.dtype)
+
+        output = torch.matmul(fp8A, fp8B)
+
+        # output is half
+
+        # 3. Save state
+        ctx.fw_code = fw_code
+        ctx.bw_code = bw_code
+        ctx.bsz = bsz
+        ctx.bsz2 = bsz2
+        ctx.dtype_A, ctx.dtype_B = A.dtype, B.dtype
+
+        if any(ctx.needs_input_grad[:2]):
+            # NOTE: we send back A, and re-quant.
+            ctx.tensors = (A, fp8B)
+        else:
+            ctx.tensors = (None, None)
+
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if ctx.is_empty:
+            return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, None, None, None, None
+
+        req_gradA, req_gradB, _, _, _, _, _ = ctx.needs_input_grad
+        A, B = ctx.tensors
+
+        grad_A, grad_B = None, None
+
+        # TODO: Fix blocksize to be output_dim
+        cgrad_out, state = F.quantize_blockwise(grad_output, code=ctx.bw_code, blocksize=ctx.bsz2)
+        fp8out = F.dequantize_blockwise(cgrad_out, state, blocksize=ctx.bsz2).to(grad_output.dtype)
+
+        # cgrad_output_2, state_2 = F.quantize(grad_output.float(), code=ctx.bw_code)
+        # fp8out_2 = F.dequantize(cgrad_output_2, state_2).to(grad_output.dtype)
+
+        # grad_output_reshape = grad_output.reshape(-1, grad_output.shape[-1]).contiguous()
+        # fp8grad_transpose, stategrad_transpose = F.vectorwise_quant(grad_output_reshape, dim=0, quant_type='vector')
+        # fp8out_transpose = (fp8grad_transpose / 7) * stategrad_transpose
+        # fp8out_transpose = fp8out_transpose.view(grad_output.shape[0], grad_output.shape[1], grad_output.shape[2])
+
+        # not supported by PyTorch. TODO: create work-around
+        if req_gradA: 
+            grad_A = torch.matmul(fp8out, B.t().to(fp8out.dtype)).to(A.dtype)
+
+        if req_gradB:
+            At = A.transpose(2, 1).contiguous()
+            # cA, state = F.quantize(At.float(), code=ctx.fw_code)
+            # fp8At = F.dequantize(cA, state).to(A.dtype)
+            grad_B = torch.matmul(At.to(grad_output.dtype), grad_output).to(B.dtype)
+
+        return grad_A, grad_B, None, None, None, None, None
+
+
+class MatMulFP8Global(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")
+
+    @staticmethod
+    def forward(ctx, A, B, out=None, fw_code=None, bw_code=None, bsz=1024, bsz2=1024):
+        # default of pytorch behavior if inputs are empty
+        ctx.is_empty = False
+        if prod(A.shape) == 0:
+            ctx.is_empty = True
+            ctx.A = A
+            ctx.B = B
+
+            B_shape = B.shape
+            if A.shape[-1] == B_shape[0]:
+                return torch.empty(A.shape[:-1] + B_shape[1:], dtype=A.dtype, device=A.device)
+            else:
+                return torch.empty(A.shape[:-1] + B_shape[:1], dtype=A.dtype, device=A.device)
+
+        # 1. Dequantize
+        # 2. MatmulnN
+        cA, state = F.quantize(A.float(), code=fw_code)
+        fp8A = F.dequantize(cA, state).to(A.dtype)
+
+        cB, state = F.quantize(B.float(), code=fw_code)
+        fp8B = F.dequantize(cB, state).to(B.dtype)
+
+        output = torch.matmul(fp8A, fp8B)
+
+        # output is half
+
+        # 3. Save state
+        ctx.fw_code = fw_code
+        ctx.bw_code = bw_code
+        ctx.bsz = bsz
+        ctx.bsz2 = bsz2
+        ctx.dtype_A, ctx.dtype_B = A.dtype, B.dtype
+
+        if any(ctx.needs_input_grad[:2]):
+            # NOTE: we send back A, and re-quant.
+            ctx.tensors = (A, fp8B)
+        else:
+            ctx.tensors = (None, None)
+
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if ctx.is_empty:
+            return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, None, None, None, None
+
+        req_gradA, req_gradB, _, _, _, _, _ = ctx.needs_input_grad
+        A, B = ctx.tensors
+
+        grad_A, grad_B = None, None
+
+        # TODO: Fix blocksize to be output_dim
+        cgrad_out, state = F.quantize(grad_output.float(), code=ctx.bw_code)
+        fp8out = F.dequantize(cgrad_out, state).to(grad_output.dtype)
+
+        # cgrad_output_2, state_2 = F.quantize(grad_output.float(), code=ctx.bw_code)
+        # fp8out_2 = F.dequantize(cgrad_output_2, state_2).to(grad_output.dtype)
+
+        # grad_output_reshape = grad_output.reshape(-1, grad_output.shape[-1]).contiguous()
+        # fp8grad_transpose, stategrad_transpose = F.vectorwise_quant(grad_output_reshape, dim=0, quant_type='vector')
+        # fp8out_transpose = (fp8grad_transpose / 7) * stategrad_transpose
+        # fp8out_transpose = fp8out_transpose.view(grad_output.shape[0], grad_output.shape[1], grad_output.shape[2])
+
+        # not supported by PyTorch. TODO: create work-around
+        if req_gradA: 
+            grad_A = torch.matmul(fp8out, B.t().to(fp8out.dtype)).to(A.dtype)
+
+        if req_gradB:
+            At = A.transpose(2, 1).contiguous()
+            cA, state = F.quantize(At.float(), code=ctx.fw_code)
+            fp8At = F.dequantize(cA, state).to(A.dtype)
+            grad_B = torch.matmul(fp8At.to(fp8out.dtype), fp8out).to(B.dtype)
+
+        return grad_A, grad_B, None, None, None, None, None
+
+
+class MatMul8bitMixed(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState()):
+        # default to pytorch behavior if inputs are empty
+        ctx.is_empty = False
+        if prod(A.shape) == 0:
+            ctx.is_empty = True
+            ctx.A = A
+            ctx.B = B
+            ctx.bias = bias
+            if A.shape[-1] == B.shape[0]:
+                return torch.empty(A.shape[:-1]+B.shape[1:], dtype=A.dtype, device=A.device)
+            else:
+                return torch.empty(A.shape[:-1]+B.shape[:1], dtype=A.dtype, device=A.device)
+
+        # 1. Quantize A
+        # 2. Quantize B
+        # 3. Matmul
+        # 4. Mixed-precision decomposition matmul
+        # 5. Save state
+        formatB = state.formatB
+        input_shape = A.shape
+        if state.outlier_pool is None:
+            state.outlier_pool = GlobalOutlierPooler.get_instance()
+
+        # Cast A to fp16
+        if A.dtype != torch.float16:
+            warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
+
+        # 1. Quantize A
+        if len(A.shape) == 3:
+            A = A.view(-1, A.shape[-1]).contiguous()
+        CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(
+            A.to(torch.float16), threshold=state.threshold
+        )
+
+        if state.threshold > 0.0 and coo_tensorA is not None:
+            if state.has_fp16_weights:
+                idx = torch.unique(coo_tensorA.colidx).long()
+                CA[:, idx] = 0
+                CAt[:, idx] = 0
+                subA = A[:, idx]
+                state.subB = B[:, idx].t().contiguous()
+                state.idx = idx
+            else:
+                if state.CxB is None:
+                    # B in in 8-bit row-major, we can transform it back to 16-bit to extract outlier dimensions
+                    # we also need to convert it to the turing/ampere format
+                    state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
+        else:
+            #print('A shape', A.shape)
+            if not state.has_fp16_weights and state.CxB is None:
+                state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
+            subA = None
+
+        # 2. Quantize B
+        if state.has_fp16_weights:
+            #print('B shape', B.shape)
+            has_grad = True if (getattr(B, "grad", None) is not None) else False
+            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.CxB is None:
+                state.reset_grads()
+                (
+                    CB,
+                    state.CBt,
+                    state.SCB,
+                    state.SCBt,
+                    coo_tensorB,
+                ) = F.double_quant(B.to(torch.float16))
+                state.CxB, state.SB = F.transform(CB, to_order=formatB)
+        else:
+            has_grad = False
+
+        if coo_tensorA is not None and not state.has_fp16_weights:
+            # extract outliers
+
+            outlier_idx = torch.unique(coo_tensorA.colidx)
+            state.idx = outlier_idx
+            # state.outlier_pool.add_outliers(outlier_idx, A.shape[-1])
+            # if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]:
+            #    # do not use pool for 2nd FFN layer
+            #    state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device)
+            # else:
+            #    state.idx = outlier_idx
+            outliers = F.extract_outliers(state.CxB, state.SB, state.idx.int())
+            state.subB = (
+                (outliers * state.SCB.view(-1, 1) / 127.0)
+                .t()
+                .contiguous()
+                .to(A.dtype)
+            )
+            CA[:, state.idx.long()] = 0
+            CAt[:, state.idx.long()] = 0
+            subA = A[:, state.idx.long()]
+
+        shapeB = state.SB[0]
+
+        if len(input_shape) == 3:
+            output_shape = (input_shape[0], input_shape[1], shapeB[0])
+        else:
+            output_shape = (input_shape[0], shapeB[0])
+
+        # 3. Matmul
+        C32A, SA = F.transform(CA, "col32")
+        out32, Sout32 = F.igemmlt(C32A, state.CxB, SA, state.SB)
+        # we apply the fused bias here
+
+        if bias is None or bias.dtype == torch.float16:
+            output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=bias)
+            output = output.to(A.dtype)
+        else:  # apply bias separately
+            output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=None)
+            output = output.to(A.dtype).add_(bias)
+
+        # 4. Mixed-precision decomposition matmul
+        if coo_tensorA is not None and subA is not None:
+            output += torch.matmul(subA, state.subB)
+
+        # 5. Save state
+        ctx.state = state
+
+        ctx.formatB = formatB
+        ctx.grad_shape = input_shape
+        ctx.dtype_A, ctx.dtype_B, ctx.dtype_bias = A.dtype, B.dtype, None if bias is None else bias.dtype
+
+        if any(ctx.needs_input_grad[:2]):
+            ctx.tensors = (CAt, subA, A)
+            ctx.tensor_states = (SCAt, state.idx)
+        else:
+            ctx.tensors = [None, None, None]
+            ctx.tensor_states = (None, None)
+            ctx.save_for_backward(None, None)
+
+
+        clone_func = torch.clone if len(output_shape) == 3 else lambda x : x
+        return clone_func(output.view(output_shape))
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if ctx.is_empty:
+            bias_grad = (None if ctx.bias is None else torch.zeros_like(ctx.bias))
+            return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None
+        req_gradA, req_gradB, _, req_gradBias, _ = ctx.needs_input_grad
+        CAt, subA, A = ctx.tensors
+        SCAt, idx = ctx.tensor_states
+        formatB = ctx.formatB
+        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()
+
+        Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output.to(torch.float16))
+
+        if req_gradB:
+            # print('back A shape', A.shape)
+            # print('grad output t shape', grad_output.t().shape)
+            grad_B = torch.matmul(grad_output.t(), A)
+
+        if req_gradA:
+            if state.CBt is not None:
+                C32grad, Sgrad = F.transform(Cgrad, "col32")
+                if state.CxBt is None:
+                    state.CxBt, state.SBt = F.transform(
+                        state.CBt, to_order=formatB, transpose=True
+                    )
+                # print('back B shape', state.CxBt.shape)
+                # print('back grad shape', C32grad.shape)
+                gradA32, SgradA32 = F.igemmlt(C32grad, state.CxBt, Sgrad, state.SBt)
+                grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape).to(ctx.dtype_A)
+
+            elif state.CB is not None:
+                CB = state.CB.to(ctx.dtype_A, copy=True).mul_(state.SCB.unsqueeze(1).mul(1. / 127.0))
+                grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A)
+            else:
+                raise Exception('State must contain either CBt or CB matrix for backward')
+
+        return grad_A, grad_B, None, grad_bias, None
+
+def get_block_sizes(input_matrix, weight_matrix):
+    input_features = input_matrix.shape[-1]
+    output_features = (weight_matrix.shape[0] if weight_matrix.shape[1] == input_features else weight_matrix.shape[1])
+    array = [4096, 2048, 1024, 512, 256, 128, 64, 0]
+    bsz, bsz2 = 1024, 1024
+    for i, k in enumerate(array):
+        if input_features > array[i + 1]:
+            bsz = k
+            break
+    for i, k in enumerate(array):
+        if output_features > array[i + 1]:
+            bsz2 = k
+            break
+
+    return bsz, bsz2
+
+
+def matmul_fp8(A: tensor, B: tensor, fw_code: tensor, bw_code: tensor, out: tensor = None, bsz : int = -1, bsz2 : int = -1):
+    if bsz == -1 or bsz2 == -1: bsz, bsz2 = get_block_sizes(A, B)
+    return MatMulFP8.apply(A, B, out, fw_code, bw_code, bsz, bsz2)
+
+def matmul_fp8_global(A: tensor, B: tensor, fw_code: tensor, bw_code: tensor, out: tensor = None, bsz : int = -1, bsz2 : int = -1):
+    if bsz == -1 or bsz2 == -1: bsz, bsz2 = get_block_sizes(A, B)
+    return MatMulFP8Global.apply(A, B, out, fw_code, bw_code, bsz, bsz2)
+
+def matmul_fp8_mixed(A: tensor, B: tensor, fw_code: tensor, bw_code: tensor, out: tensor = None, bsz : int = -1, bsz2 : int = -1):
+    if bsz == -1 or bsz2 == -1: bsz, bsz2 = get_block_sizes(A, B)
+    return MatMulFP8Mixed.apply(A, B, out, fw_code, bw_code, bsz, bsz2)
+
+
+def switchback_bnb(
+    A: tensor,
+    B: tensor,
+    out: tensor = None,
+    state: MatmulLtState = None,
+    threshold=0.0,
+    bias=None
+):
+    state = state or MatmulLtState()
+    if threshold > 0.0:
+        state.threshold = threshold
+    return MatMul8bitMixed.apply(A, B, out, bias, state)

bitsandbytes/triton/dequantize_rowwise.py

@@ -0,0 +1,58 @@
+import math
+import torch
+import time
+import triton
+import triton.language as tl
+from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
+
+# rowwise quantize
+
+# TODO: autotune this better.
+@triton.autotune(
+        configs=[
+            triton.Config({}, num_stages=1, num_warps=8),
+            triton.Config({}, num_stages=2, num_warps=8),
+            triton.Config({}, num_stages=4, num_warps=8),
+            triton.Config({}, num_stages=8, num_warps=8),
+            triton.Config({}, num_stages=1),
+            triton.Config({}, num_stages=2),
+            triton.Config({}, num_stages=4),
+            triton.Config({}, num_stages=8),
+            triton.Config({}, num_warps=1),
+            triton.Config({}, num_warps=2),
+            triton.Config({}, num_warps=4),
+            triton.Config({}, num_warps=8),
+        ],
+        key=['n_elements']
+)
+@triton.jit
+def _dequantize_rowwise(
+    x_ptr,
+    state_x,
+    output_ptr,
+    inv_127,
+    n_elements,
+    BLOCK_SIZE: tl.constexpr,
+    P2: tl.constexpr,
+):
+    pid = tl.program_id(axis=0)
+    block_start = pid * BLOCK_SIZE
+    arange = tl.arange(0, P2)
+    offsets = block_start + arange
+    row_mask = arange < BLOCK_SIZE
+    x = tl.load(x_ptr + offsets, mask=row_mask)
+    max_val = tl.load(state_x + pid)
+    output = max_val * x * inv_127
+    tl.store(output_ptr + offsets, output, mask=row_mask)
+    
+
+def dequantize_rowwise(x: torch.Tensor, state_x: torch.Tensor):
+    output = torch.empty(*x.shape, device=x.device, dtype=torch.float16)
+
+    P2 = int(2 ** (math.ceil(math.log2(x.shape[1]))))
+
+    assert x.is_cuda and output.is_cuda
+    n_elements = output.numel()
+    grid = lambda meta: (x.shape[0],)
+    _dequantize_rowwise[grid](x, state_x, output, 1./127, n_elements, BLOCK_SIZE=x.shape[1], P2=P2)
+    return output

bitsandbytes/triton/int8_matmul_mixed_dequanitze.py

@@ -0,0 +1,158 @@
+import torch
+
+import triton
+import triton.language as tl
+from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
+
+
+# This is a matmul kernel based on triton.ops.matmul
+# It is modified to support rowwise quantized input and global quantized weight
+# It's purpose is fused matmul then dequantize
+# It does support bias.
+
+def init_to_zero(name):
+    return lambda nargs: nargs[name].zero_()
+
+def get_configs_io_bound():
+    configs = []
+    for num_stages in [2, 3, 4, 5, 6]:
+        for block_m in [16, 32]:
+            for block_k in [32, 64]:
+                for block_n in [32, 64, 128, 256]:
+                    num_warps = 2 if block_n <= 64 else 4
+                    configs.append(
+                        triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': 1},
+                                      num_stages=num_stages, num_warps=num_warps))
+                    # split_k
+                    for split_k in [2, 4, 8, 16]:
+                        configs.append(triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': split_k},
+                                                     num_stages=num_stages, num_warps=num_warps, pre_hook=init_to_zero('C')))
+    return configs
+
+
+@triton.autotune(
+    configs=[
+        # basic configs for compute-bound matmuls
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
+        # good for int8
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
+    ] + get_configs_io_bound(),
+    key=['M', 'N', 'K'],
+    prune_configs_by={
+        'early_config_prune': early_config_prune,
+        'perf_model': estimate_matmul_time,
+        'top_k': 10
+    },
+)
+@triton.heuristics({
+    'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
+})
+@triton.jit
+def _int8_matmul_mixed_dequantize(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor: tl.constexpr, has_bias : tl.constexpr,
+            stride_am, stride_ak,
+            stride_bk, stride_bn,
+            stride_cm, stride_cn,
+            BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
+            GROUP_M: tl.constexpr, SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr,
+            ACC_TYPE: tl.constexpr
+            ):
+    # matrix multiplication
+    pid = tl.program_id(0)
+    pid_z = tl.program_id(1)
+    grid_m = tl.cdiv(M, BLOCK_M)
+    grid_n = tl.cdiv(N, BLOCK_N)
+    # re-order program ID for better L2 performance
+    width = GROUP_M * grid_n
+    group_id = pid // width
+    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
+    pid_m = group_id * GROUP_M + (pid % group_size)
+    pid_n = (pid % width) // (group_size)
+    # do matrix multiplication
+    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
+    ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
+    rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
+    rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K)
+    # pointers
+    A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
+    B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
+
+    # rematerialize rm and rn to save registers
+    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
+
+    w_factor = tl.load(state_w_ptr)
+    x_factor = tl.load(state_x_ptr + ram)[:, None]
+
+    # acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
+    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.int32)
+    for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)):
+        if EVEN_K:
+            a = tl.load(A)
+            b = tl.load(B)
+        else:
+            k_remaining = K - k * (BLOCK_K * SPLIT_K)
+            a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.)
+            b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.)
+        acc += tl.dot(a, b)
+        A += BLOCK_K * SPLIT_K * stride_ak
+        B += BLOCK_K * SPLIT_K * stride_bk
+    
+    acc = (w_factor * (x_factor * (acc * divfactor)))
+    acc = acc.to(C.dtype.element_ty)
+
+    # conditionally add bias
+    if has_bias:
+        bias = tl.load(bias + rn).to(C.dtype.element_ty)
+        acc = acc + bias[None, :]
+
+    C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn)
+    mask = (rm < M)[:, None] & (rn < N)[None, :]
+    # handles write-back with reduction-splitting
+    if SPLIT_K == 1:
+        tl.store(C, acc, mask=mask)
+    else:
+        tl.atomic_add(C, acc, mask=mask)
+
+
+def int8_matmul_mixed_dequanitze(a, b, state_x, state_w, bias):
+    device = a.device
+    divfactor = 1. / (127. * 127.)
+    has_bias = 0 if bias is None else 1
+    # handle non-contiguous inputs if necessary
+    if a.stride(0) > 1 and a.stride(1) > 1:
+        a = a.contiguous()
+    if b.stride(0) > 1 and b.stride(1) > 1:
+        b = b.contiguous()
+    # checks constraints
+    assert a.shape[1] == b.shape[0], "incompatible dimensions"
+    M, K = a.shape
+    _, N = b.shape
+    # allocates output
+    c = torch.empty((M, N), device=device, dtype=torch.float16)
+    # accumulator types
+    ACC_TYPE = tl.float32 #if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32
+    # launch int8_matmul_mixed_dequantize kernel
+    grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K'])
+    _int8_matmul_mixed_dequantize[grid](a, b, c, bias, state_x, state_w, M, N, K, divfactor, has_bias,
+                    a.stride(0), a.stride(1),
+                    b.stride(0), b.stride(1),
+                    c.stride(0), c.stride(1),
+                    GROUP_M=8, ACC_TYPE=ACC_TYPE)
+    return c

bitsandbytes/triton/int8_matmul_rowwise_dequantize.py

@@ -0,0 +1,159 @@
+import torch
+
+import triton
+import triton.language as tl
+from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
+
+# This is a matmul kernel based on triton.ops.matmul
+# It is modified to support rowwise quantized input and columnwise quantized weight
+# It's purpose is fused matmul then dequantize
+# It does support bias.
+
+def init_to_zero(name):
+    return lambda nargs: nargs[name].zero_()
+
+
+def get_configs_io_bound():
+    configs = []
+    for num_stages in [2, 3, 4, 5, 6]:
+        for block_m in [16, 32]:
+            for block_k in [32, 64]:
+                for block_n in [32, 64, 128, 256]:
+                    num_warps = 2 if block_n <= 64 else 4
+                    configs.append(
+                        triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': 1},
+                                      num_stages=num_stages, num_warps=num_warps))
+                    # split_k
+                    for split_k in [2, 4, 8, 16]:
+                        configs.append(triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': split_k},
+                                                     num_stages=num_stages, num_warps=num_warps, pre_hook=init_to_zero('C')))
+    return configs
+
+
+@triton.autotune(
+    configs=[
+        # basic configs for compute-bound matmuls
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
+        # good for int8
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
+        triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
+        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
+    ] + get_configs_io_bound(),
+    key=['M', 'N', 'K'],
+    prune_configs_by={
+        'early_config_prune': early_config_prune,
+        'perf_model': estimate_matmul_time,
+        'top_k': 10
+    },
+)
+@triton.heuristics({
+    'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
+})
+@triton.jit
+def _int8_matmul_rowwise_dequantize(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor, has_bias : tl.constexpr,
+            stride_am, stride_ak,
+            stride_bk, stride_bn,
+            stride_cm, stride_cn,
+            BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
+            GROUP_M: tl.constexpr, SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr,
+            ACC_TYPE: tl.constexpr
+            ):
+    # matrix multiplication
+    pid = tl.program_id(0)
+    pid_z = tl.program_id(1)
+    grid_m = tl.cdiv(M, BLOCK_M)
+    grid_n = tl.cdiv(N, BLOCK_N)
+    # re-order program ID for better L2 performance
+    width = GROUP_M * grid_n
+    group_id = pid // width
+    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
+    pid_m = group_id * GROUP_M + (pid % group_size)
+    pid_n = (pid % width) // (group_size)
+    # do matrix multiplication
+    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
+    ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
+    rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
+    rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K)
+    # pointers
+    A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
+    B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
+
+    # rematerialize rm and rn to save registers
+    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
+
+    w_factor = tl.load(state_w_ptr + rbn)[None, :]
+    x_factor = tl.load(state_x_ptr + ram)[:, None]
+
+    # acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
+    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.int32)
+    for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)):
+        if EVEN_K:
+            a = tl.load(A)
+            b = tl.load(B)
+        else:
+            k_remaining = K - k * (BLOCK_K * SPLIT_K)
+            a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.)
+            b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.)
+        acc += tl.dot(a, b)
+        A += BLOCK_K * SPLIT_K * stride_ak
+        B += BLOCK_K * SPLIT_K * stride_bk
+    
+    acc = (w_factor * (x_factor * (acc * divfactor)))
+    acc = acc.to(C.dtype.element_ty)
+
+    if has_bias:
+        bias = tl.load(bias + rn).to(C.dtype.element_ty)
+        acc = acc + bias[None, :]
+
+    C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn)
+    mask = (rm < M)[:, None] & (rn < N)[None, :]
+    # handles write-back with reduction-splitting
+    if SPLIT_K == 1:
+        tl.store(C, acc, mask=mask)
+    else:
+        tl.atomic_add(C, acc, mask=mask)
+
+
+def int8_matmul_rowwise_dequantize(a, b, state_x, state_w, bias):
+    divfactor = 1. / (127. * 127.)
+
+    has_bias = 0 if bias is None else 1
+
+    device = a.device
+    # handle non-contiguous inputs if necessary
+    if a.stride(0) > 1 and a.stride(1) > 1:
+        a = a.contiguous()
+    if b.stride(0) > 1 and b.stride(1) > 1:
+        b = b.contiguous()
+    # checks constraints
+    assert a.shape[1] == b.shape[0], "incompatible dimensions"
+    M, K = a.shape
+    _, N = b.shape
+    # allocates output
+    c = torch.empty((M, N), device=device, dtype=torch.float16)
+    # accumulator types
+    ACC_TYPE = tl.float32 #if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32
+    # launch int8_matmul_rowwise_dequantize kernel
+    grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K'])
+    _int8_matmul_rowwise_dequantize[grid](a, b, c, bias, state_x, state_w, M, N, K, divfactor, has_bias,
+                    a.stride(0), a.stride(1),
+                    b.stride(0), b.stride(1),
+                    c.stride(0), c.stride(1),
+                    GROUP_M=8, ACC_TYPE=ACC_TYPE)
+    return c

bitsandbytes/triton/quantize_columnwise_and_transpose.py

@@ -0,0 +1,68 @@
+import math
+import torch
+import time
+import triton
+import triton.language as tl
+from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
+
+# This kernel does fused columnwise quantization and transpose.
+
+# TODO: autotune this better.
+@triton.autotune(
+        configs=[
+            triton.Config({}, num_stages=1),
+            triton.Config({}, num_stages=2),
+            triton.Config({}, num_stages=4),
+            triton.Config({}, num_stages=8),
+            triton.Config({}, num_stages=16),
+            triton.Config({}, num_stages=1, num_warps=8),
+            triton.Config({}, num_stages=2, num_warps=8),
+            triton.Config({}, num_stages=4, num_warps=8),
+            triton.Config({}, num_stages=8, num_warps=8),
+            triton.Config({}, num_stages=16, num_warps=8),
+            triton.Config({}, num_warps=1),
+            triton.Config({}, num_warps=2),
+            triton.Config({}, num_warps=4),
+            triton.Config({}, num_warps=8),
+        ],
+        key=['n_elements']
+)
+@triton.jit
+def _quantize_columnwise_and_transpose(
+    x_ptr,
+    output_ptr,
+    output_maxs,
+    n_elements,
+    M : tl.constexpr, N : tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+    P2: tl.constexpr,
+):
+    pid = tl.program_id(axis=0)
+    block_start = pid
+    p2_arange = tl.arange(0, P2)
+    p2_arange_mask = p2_arange < M
+    arange =  p2_arange * N
+    offsets = block_start + arange
+    x = tl.load(x_ptr + offsets, mask=p2_arange_mask)
+    abs_x = tl.abs(x)
+    max_val = tl.max(tl.where(p2_arange_mask, abs_x, 0), axis=0)
+    output = tl.libdevice.llrint(127. * (x / max_val))
+
+    new_start = pid * M 
+    new_offsets = new_start + p2_arange
+    tl.store(output_ptr + new_offsets, output, mask=p2_arange_mask)
+    tl.store(output_maxs + pid, max_val)
+
+def quantize_columnwise_and_transpose(x: torch.Tensor):
+    M, N = x.shape
+    output = torch.empty(N, M, device=x.device, dtype=torch.int8)
+    output_maxs = torch.empty(x.shape[1], device=x.device, dtype=torch.float16)
+
+    P2 = int(2 ** (math.ceil(math.log2(M))))
+
+    assert x.is_cuda and output.is_cuda
+    n_elements = output.numel()
+    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
+    _quantize_columnwise_and_transpose[grid](x, output, output_maxs, n_elements, M, N, BLOCK_SIZE=M, P2=P2)
+    return output, output_maxs
+

bitsandbytes/triton/quantize_global.py

@@ -0,0 +1,100 @@
+import math
+import torch
+import time
+import triton
+import triton.language as tl
+from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
+
+# global quantize
+@triton.autotune(
+        configs=[
+            triton.Config({'BLOCK_SIZE': 1024,}, num_warps=4),
+            triton.Config({'BLOCK_SIZE': 2048,}, num_stages=1),
+
+        ],
+        key=['n_elements']
+)
+@triton.jit
+def _quantize_global(
+    x_ptr,
+    absmax_inv_ptr,
+    output_ptr,
+    n_elements,
+    BLOCK_SIZE: tl.constexpr,
+):
+    pid = tl.program_id(axis=0)
+    block_start = pid * BLOCK_SIZE
+    offsets = block_start + tl.arange(0, BLOCK_SIZE)
+    mask = offsets < n_elements
+    x = tl.load(x_ptr + offsets, mask=mask)
+    absmax_inv = tl.load(absmax_inv_ptr)
+    output = tl.libdevice.llrint(127. * (x * absmax_inv))
+    tl.store(output_ptr + offsets, output, mask=mask)
+
+def quantize_global(x: torch.Tensor):
+    absmax = x.abs().max().unsqueeze(0)
+    absmax_inv = 1./ absmax
+    output = torch.empty(*x.shape, device='cuda', dtype=torch.int8)
+    assert x.is_cuda and output.is_cuda
+    n_elements = output.numel()
+    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
+    _quantize_global[grid](x, absmax_inv, output, n_elements)
+    return output, absmax
+
+
+# global quantize and transpose
+@triton.autotune(
+        configs=[
+            triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'GROUP_M': 8}, num_warps=4),
+            triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'GROUP_M': 8}, num_warps=4),
+
+            # ...
+        ],
+        key=['M', 'N']
+)
+@triton.jit
+def _quantize_global_transpose(A, absmax_inv_ptr, B, stride_am, stride_an, stride_bn, stride_bm, M, N, 
+                      BLOCK_M : tl.constexpr, 
+                      BLOCK_N : tl.constexpr, 
+                      GROUP_M : tl.constexpr):
+    pid = tl.program_id(0)
+    grid_m = (M + BLOCK_M - 1) // BLOCK_M
+    grid_n = (N + BLOCK_N - 1) // BLOCK_N
+    
+    width = GROUP_M * grid_n
+    group_id = pid // width
+    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
+    pid_m = group_id * GROUP_M + (pid % group_size)
+    pid_n = (pid % width) // group_size
+    
+    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
+    A = A + (rm[:, None] * stride_am + rn[None, :] * stride_an)
+    mask = (rm < M)[:, None] & (rn < N)[None, :]
+    a = tl.load(A, mask=mask)
+    absmax_inv = tl.load(absmax_inv_ptr)
+    
+    # rematerialize to save registers
+    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
+    B = B + (rm[:, None] * stride_bm + rn[None, :] * stride_bn)
+    mask = (rm < M)[:, None] & (rn < N)[None, :]
+
+    output = tl.libdevice.llrint(127. * (a * absmax_inv))
+
+    tl.store(B, output, mask=mask)
+
+def quantize_global_transpose(input):
+    absmax = input.abs().max().unsqueeze(0)
+    absmax_inv = 1./ absmax
+    M, N = input.shape
+    out = torch.empty(N, M, device='cuda', dtype=torch.int8)
+    
+    assert out.size(0) == N and out.size(1) == M
+    assert input.stride(0) == 1 or input.stride(1) == 1
+    assert out.stride(0) == 1 or out.stride(1) == 1
+    
+    grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']),)
+    _quantize_global_transpose[grid](input, absmax_inv, out, input.stride(0), input.stride(1), out.stride(0), out.stride(1), M, N)
+    return out, absmax
+

bitsandbytes/triton/quantize_rowwise.py

@@ -0,0 +1,61 @@
+import math
+import torch
+import time
+import triton
+import triton.language as tl
+from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
+
+# rowwise quantize
+
+# TODO: autotune this better.
+@triton.autotune(
+        configs=[
+            triton.Config({}, num_stages=1, num_warps=8),
+            triton.Config({}, num_stages=2, num_warps=8),
+            triton.Config({}, num_stages=4, num_warps=8),
+            triton.Config({}, num_stages=8, num_warps=8),
+            triton.Config({}, num_stages=1),
+            triton.Config({}, num_stages=2),
+            triton.Config({}, num_stages=4),
+            triton.Config({}, num_stages=8),
+            triton.Config({}, num_warps=1),
+            triton.Config({}, num_warps=2),
+            triton.Config({}, num_warps=4),
+            triton.Config({}, num_warps=8),
+        ],
+        key=['n_elements']
+)
+@triton.jit
+def _quantize_rowwise(
+    x_ptr,
+    output_ptr,
+    output_maxs,
+    n_elements,
+    BLOCK_SIZE: tl.constexpr,
+    P2: tl.constexpr,
+):
+    pid = tl.program_id(axis=0)
+    block_start = pid * BLOCK_SIZE
+    arange = tl.arange(0, P2)
+    offsets = block_start + arange
+    row_mask = arange < BLOCK_SIZE
+    x = tl.load(x_ptr + offsets, mask=row_mask)
+    
+    abs_x = tl.abs(x)
+    max_val = tl.max(tl.where(row_mask, abs_x, 0), axis=0)
+    output = tl.libdevice.llrint(127. * (x / max_val))
+    tl.store(output_ptr + offsets, output, mask=row_mask)
+    tl.store(output_maxs + pid, max_val)
+
+def quantize_rowwise(x: torch.Tensor):
+    output = torch.empty(*x.shape, device=x.device, dtype=torch.int8)
+    output_maxs = torch.empty(x.shape[0], device=x.device, dtype=torch.float16)
+
+    P2 = int(2 ** (math.ceil(math.log2(x.shape[1]))))
+
+    assert x.is_cuda and output.is_cuda
+    n_elements = output.numel()
+    grid = lambda meta: (x.shape[0],)
+    _quantize_rowwise[grid](x, output, output_maxs, n_elements, BLOCK_SIZE=x.shape[1], P2=P2)
+    return output, output_maxs
+