Add CUDA kernel support for 4-bit quantization with blocksize=32

bitsandbytes/backends/cuda/ops.py

@@ -214,7 +214,7 @@ def _(A: torch.Tensor, code: torch.Tensor, blocksize: int) -> tuple[torch.Tensor
     if ROCM_WARP_SIZE_64:
         torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128])
     else:
-        torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64])
+        torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64, 32])
 
     torch._check(code.dtype == torch.float32, lambda: f"code must be float32, got {code.dtype}")
 
@@ -272,7 +272,7 @@ def _dequantize_blockwise_impl(
     if ROCM_WARP_SIZE_64:
         torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128])
     else:
-        torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64])
+        torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64, 32])
 
     torch._check(A.dtype == torch.uint8, lambda: f"A must be uint8, got {A.dtype}")
     torch._check(
@@ -306,7 +306,7 @@ def _(
     if ROCM_WARP_SIZE_64:
         torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128])
     else:
-        torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64])
+        torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64, 32])
 
     torch._check(quant_type in ["fp4", "nf4"])
     torch._check(
@@ -388,7 +388,7 @@ def _dequantize_4bit_impl(
     if ROCM_WARP_SIZE_64:
         torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128])
     else:
-        torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64])
+        torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64, 32])
 
     torch._check(quant_type in ["fp4", "nf4"])
     torch._check(

bitsandbytes/functional.py

@@ -842,7 +842,7 @@ def quantize_4bit(
         out (`torch.Tensor`, *optional*): A tensor to use to store the result.
         blocksize (`int`, *optional*):
             The size of the blocks. Defaults to 128 on ROCm and 64 otherwise.
-            Valid values are 64, 128, 256, 512, 1024, 2048, and 4096.
+            Valid values are 32, 64, 128, 256, 512, 1024, 2048, and 4096.
         compress_statistics (`bool`, *optional*): Whether to additionally quantize the absmax values. Defaults to False.
         quant_type (`str`, *optional*): The data type to use: `nf4` or `fp4`. Defaults to `fp4`.
         quant_storage (`torch.dtype`, *optional*): The dtype of the tensor used to store the result. Defaults to `torch.uint8`.
@@ -953,7 +953,7 @@ def dequantize_4bit(
         out (`torch.Tensor`, *optional*): A tensor to use to store the result.
         blocksize (`int`, *optional*):
             The size of the blocks. Defaults to 128 on ROCm and 64 otherwise.
-            Valid values are 64, 128, 256, 512, 1024, 2048, and 4096.
+            Valid values are 32, 64, 128, 256, 512, 1024, 2048, and 4096.
         quant_type (`str`, *optional*): The data type to use: `nf4` or `fp4`. Defaults to `fp4`.
 
     Raises:

csrc/kernels.cu

@@ -423,6 +423,92 @@ __global__ void kQuantizeBlockwise(
     }
 }
 
+// Specialized kernel for blocksize=32 with 4-bit quantization
+// Processes 2 blocks of 32 values per warp to maintain full thread utilization
+// Uses 32 threads total: threads 0-15 handle block 0, threads 16-31 handle block 1
+template <typename T, int DATA_TYPE>
+__global__ void kQuantizeBlockwise32(
+    float* code, T* __restrict__ const A, float* absmax, unsigned char* out, float* __restrict__ const rand,
+    const int rand_offset, const int n
+) {
+    // Fixed parameters for blocksize=32 with 4-bit
+    constexpr int BLOCK_SIZE = 32;        // Size of each quantization block
+    constexpr int NUM_PER_TH = 2;         // Values per thread (for 4-bit packing)
+    constexpr int THREADS = 32;           // Total threads (full warp)
+    constexpr int THREADS_PER_BLOCK = 16; // Threads handling each quantization block
+
+    // Each CUDA thread block processes 2 quantization blocks of 32 values each
+    const int base_idx = blockIdx.x * BLOCK_SIZE * 2; // 2 blocks per CUDA block
+
+    T vals[NUM_PER_TH];
+    unsigned char qvals[NUM_PER_TH / 2]; // For 4-bit: 2 values per byte
+    float local_abs_max = 0.0f;
+
+    // Determine which quantization block this thread belongs to (0 or 1)
+    const int block_id = threadIdx.x / THREADS_PER_BLOCK;        // 0 for threads 0-15, 1 for threads 16-31
+    const int local_thread_id = threadIdx.x % THREADS_PER_BLOCK; // Thread ID within the block (0-15)
+
+    typedef cub::BlockLoad<T, THREADS, NUM_PER_TH, cub::BLOCK_LOAD_WARP_TRANSPOSE> LoadT;
+    typedef cub::BlockStore<unsigned char, THREADS, NUM_PER_TH / 2, cub::BLOCK_STORE_WARP_TRANSPOSE> StoreChar;
+    typedef cub::WarpReduce<float> WarpReduce;
+
+    __shared__ typename LoadT::TempStorage loadt;
+    __shared__ typename StoreChar::TempStorage storec;
+    __shared__ typename WarpReduce::TempStorage warp_reduce[2]; // One for each warp half
+    __shared__ float smem_absmax_value[2];                      // Store 2 absmax values
+
+    const int i = base_idx + block_id * BLOCK_SIZE;
+
+    // Early exit if this quantization block is out of bounds
+    if (i >= n)
+        return;
+
+    // Load 64 values total (32 threads × 2 values each)
+    __syncthreads();
+    LoadT(loadt).Load(&(A[base_idx]), vals, min(BLOCK_SIZE * 2, n - base_idx), (T)0.0f);
+
+    // Each thread computes max of its NUM_PER_TH values
+    local_abs_max = -FLT_MAX;
+#pragma unroll NUM_PER_TH
+    for (int j = 0; j < NUM_PER_TH; j++)
+        local_abs_max = fmaxf(local_abs_max, fabsf((float)vals[j]));
+
+    // Warp-level reduction within each half (threads 0-15 and 16-31 separately)
+    local_abs_max = WarpReduce(warp_reduce[block_id]).Reduce(local_abs_max, CUB_REDUCTIONOP_MAX);
+
+    // First thread of each warp half stores the absmax
+    if (local_thread_id == 0) {
+        smem_absmax_value[block_id] = 1.0f / local_abs_max;
+        absmax[blockIdx.x * 2 + block_id] = local_abs_max;
+    }
+    __syncthreads();
+
+    // Broadcast absmax to all threads in each half
+    local_abs_max = smem_absmax_value[block_id];
+
+    // Quantize values based on data type
+    switch (DATA_TYPE) {
+    case FP4:
+#pragma unroll NUM_PER_TH
+        for (int j = 0; j < NUM_PER_TH / 2; j++) {
+            qvals[j] = dQuantizeFP4(((float)vals[2 * j]) * local_abs_max) << 4;
+            qvals[j] |= dQuantizeFP4(((float)vals[2 * j + 1]) * local_abs_max);
+        }
+        break;
+    case NF4:
+#pragma unroll NUM_PER_TH
+        for (int j = 0; j < NUM_PER_TH / 2; j++) {
+            qvals[j] = dQuantizeNF4(((float)vals[2 * j]) * local_abs_max) << 4;
+            qvals[j] |= dQuantizeNF4(((float)vals[2 * j + 1]) * local_abs_max);
+        }
+        break;
+    }
+
+    // Store quantized values (all 32 threads write their outputs)
+    __syncthreads();
+    StoreChar(storec).Store(&(out[base_idx / 2]), qvals, min((BLOCK_SIZE * 2 + 1) / 2, (n - base_idx + 1) / 2));
+}
+
 template <typename T, int TILE_SIZE, int THREADS, int NUM_PER_TH, int DATA_TYPE>
 __global__ void
     kDequantizeBlockwise(float* code, unsigned char* A, float* absmax, T* out, const int blocksize, const int n) {
@@ -2440,9 +2526,24 @@ MAKE_kQuantizeBlockwise(__nv_bfloat16, 256, 2, 0, NF4)
 MAKE_kQuantizeBlockwise(__nv_bfloat16, 128, 2, 0, NF4)
 MAKE_kQuantizeBlockwise(__nv_bfloat16, 64, 2, 0, NF4)
 
-template __global__ void kDequantizeBlockwise<half, 512, 64, 8, FP4>(
-    float* code, unsigned char* A, float* absmax, half* out, const int blocksize, const int n
-);
+// Template instantiations for blocksize=32 specialized kernel (4-bit only)
+#define MAKE_kQuantizeBlockwise32(dtype, data_type_name)                                                               \
+    template __global__ void kQuantizeBlockwise32<dtype, data_type_name>(                                              \
+        float* code, dtype* __restrict__ const A, float* absmax, unsigned char* out, float* __restrict__ const rand,   \
+        const int rand_offset, const int n                                                                             \
+    );
+
+// FP4 instantiations for blocksize=32
+MAKE_kQuantizeBlockwise32(half, FP4) MAKE_kQuantizeBlockwise32(float, FP4) MAKE_kQuantizeBlockwise32(__nv_bfloat16, FP4)
+
+    // NF4 instantiations for blocksize=32
+    MAKE_kQuantizeBlockwise32(half, NF4) MAKE_kQuantizeBlockwise32(float, NF4) MAKE_kQuantizeBlockwise32(
+        __nv_bfloat16, NF4
+    )
+
+        template __global__ void kDequantizeBlockwise<half, 512, 64, 8, FP4>(
+            float* code, unsigned char* A, float* absmax, half* out, const int blocksize, const int n
+        );
 template __global__ void kDequantizeBlockwise<half, 512, 64, 8, General8bit>(
     float* code, unsigned char* A, float* absmax, half* out, const int blocksize, const int n
 );

csrc/kernels.cuh

@@ -17,6 +17,11 @@ __global__ void kQuantizeBlockwise(
     float* code, T* __restrict__ const A, float* absmax, unsigned char* out, float* __restrict__ const rand,
     const int rand_offset, const int n
 );
+template <typename T, int DATA_TYPE>
+__global__ void kQuantizeBlockwise32(
+    float* code, T* __restrict__ const A, float* absmax, unsigned char* out, float* __restrict__ const rand,
+    const int rand_offset, const int n
+);
 template <typename T, int BLOCK_SIZE, int THREADS, int NUM_PER_TH, int DATA_TYPE>
 __global__ void
     kDequantizeBlockwise(float* code, unsigned char* A, float* absmax, T* out, const int blocksize, const int n);

csrc/ops.cu

@@ -50,6 +50,14 @@ void quantizeBlockwise(
         kQuantizeBlockwise<T, 128, 2, 0, DATA_TYPE><<<num_blocks, 64>>>(code, A, absmax, out, rand, rand_offset, n);
     else if (blocksize == 64)
         kQuantizeBlockwise<T, 64, 2, 0, DATA_TYPE><<<num_blocks, 32>>>(code, A, absmax, out, rand, rand_offset, n);
+    else if (blocksize == 32) {
+        // For 4-bit: use specialized kernel (kQuantizeBlockwise32) that processes 2 blocks per warp
+        // Each CUDA block handles 2 quantization blocks, so divide num_blocks by 2
+        if (DATA_TYPE > 0) {
+            int num_blocks_adjusted = (num_blocks + 1) / 2;
+            kQuantizeBlockwise32<T, DATA_TYPE><<<num_blocks_adjusted, 32>>>(code, A, absmax, out, rand, rand_offset, n);
+        }
+    }
 
     CUDA_CHECK_RETURN(cudaPeekAtLastError());
 }

tests/test_functional.py

@@ -1098,7 +1098,7 @@ class TestQuantize4BitFunctional:
     @pytest.mark.parametrize("quant_type", ["fp4", "nf4"])
     @pytest.mark.parametrize(
         "blocksize",
-        [64, 128, 256, 512, 1024, 2048, 4096] if not ROCM_WARP_SIZE_64 else [128, 256, 512, 1024, 2048, 4096],
+        [32, 64, 128, 256, 512, 1024, 2048, 4096] if not ROCM_WARP_SIZE_64 else [128, 256, 512, 1024, 2048, 4096],
     )
     def test_4bit_quant(self, device, dtype, quant_type, blocksize):
         if device == "hpu" and not is_supported_on_hpu(quant_type, dtype):
@@ -1122,6 +1122,7 @@ def test_4bit_quant(self, device, dtype, quant_type, blocksize):
         error_dict["fp4"] = dict()
         error_dict["nf4"] = dict()
         error_dict["fp4"]["err"] = {
+            32: 0.092737,
             64: 0.096545,
             128: 0.102947,
             256: 0.108685,
@@ -1131,6 +1132,7 @@ def test_4bit_quant(self, device, dtype, quant_type, blocksize):
             4096: 0.129573,
         }
         error_dict["fp4"]["rel_err"] = {
+            32: 0.251279,
             64: 0.260130,
             128: 0.275734,
             256: 0.289842,
@@ -1141,6 +1143,7 @@ def test_4bit_quant(self, device, dtype, quant_type, blocksize):
         }
 
         error_dict["nf4"]["err"] = {
+            32: 0.070270,
             64: 0.072792,
             128: 0.076835,
             256: 0.080326,
@@ -1150,6 +1153,7 @@ def test_4bit_quant(self, device, dtype, quant_type, blocksize):
             4096: 0.092537,
         }
         error_dict["nf4"]["rel_err"] = {
+            32: 0.196508,
             64: 0.203299,
             128: 0.215252,
             256: 0.226044,
@@ -1168,7 +1172,9 @@ def test_4bit_quant(self, device, dtype, quant_type, blocksize):
 
     @pytest.mark.parametrize("device", get_available_devices())
     @pytest.mark.parametrize("quant_type", ["fp4", "nf4"])
-    @pytest.mark.parametrize("blocksize", [64, 128] if not ROCM_WARP_SIZE_64 else [128], ids=id_formatter("blocksize"))
+    @pytest.mark.parametrize(
+        "blocksize", [32, 64, 128] if not ROCM_WARP_SIZE_64 else [128], ids=id_formatter("blocksize")
+    )
     @pytest.mark.parametrize("dtype", [torch.float32, torch.float16], ids=describe_dtype)
     def test_4bit_compressed_stats(self, device, quant_type, blocksize, dtype):
         if device == "hpu" and not is_supported_on_hpu(quant_type, dtype):
@@ -1205,7 +1211,9 @@ def test_4bit_compressed_stats(self, device, quant_type, blocksize, dtype):
     @pytest.mark.skipif(not get_available_devices(no_cpu=True), reason="No accelerator device")
     @pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16], ids=describe_dtype)
     @pytest.mark.parametrize("quant_type", ["fp4", "nf4"])
-    @pytest.mark.parametrize("blocksize", [64, 128] if not ROCM_WARP_SIZE_64 else [128], ids=id_formatter("blocksize"))
+    @pytest.mark.parametrize(
+        "blocksize", [32, 64, 128] if not ROCM_WARP_SIZE_64 else [128], ids=id_formatter("blocksize")
+    )
     def test_4bit_quant_large(self, device, dtype, quant_type, blocksize):
         """
         Test that we can successfully quantize a large tensor. Note that the following limitations apply:

tests/test_linear4bit.py

@@ -193,7 +193,7 @@ def test_linear_serialization(
 
 @pytest.mark.parametrize("device", get_available_devices())
 @pytest.mark.parametrize("quant_type", ["nf4", "fp4"])
-@pytest.mark.parametrize("blocksize", [64, 128] if not ROCM_WARP_SIZE_64 else [128])
+@pytest.mark.parametrize("blocksize", [32, 64, 128] if not ROCM_WARP_SIZE_64 else [128])
 @pytest.mark.parametrize("compress_statistics", TRUE_FALSE, ids=id_formatter("compress_statistics"))
 def test_copy_param(device, quant_type, blocksize, compress_statistics):
     if device == "hpu" and not is_supported_on_hpu(quant_type):
@@ -250,7 +250,7 @@ def test_params4bit_torch_chunk_split(device, quant_type):
 
 @pytest.mark.parametrize("device", get_available_devices())
 @pytest.mark.parametrize("quant_type", ["nf4", "fp4"])
-@pytest.mark.parametrize("blocksize", [64, 128] if not ROCM_WARP_SIZE_64 else [128])
+@pytest.mark.parametrize("blocksize", [32, 64, 128] if not ROCM_WARP_SIZE_64 else [128])
 @pytest.mark.parametrize("compress_statistics", TRUE_FALSE, ids=id_formatter("compress_statistics"))
 def test_deepcopy_param(device, quant_type, blocksize, compress_statistics):
     if device == "hpu" and not is_supported_on_hpu(quant_type):
@@ -279,7 +279,7 @@ def test_deepcopy_param(device, quant_type, blocksize, compress_statistics):
 
 @pytest.mark.parametrize("device", get_available_devices())
 @pytest.mark.parametrize("quant_type", ["nf4", "fp4"])
-@pytest.mark.parametrize("blocksize", [64, 128] if not ROCM_WARP_SIZE_64 else [128])
+@pytest.mark.parametrize("blocksize", [32, 64, 128] if not ROCM_WARP_SIZE_64 else [128])
 @pytest.mark.parametrize("compress_statistics", TRUE_FALSE, ids=id_formatter("compress_statistics"))
 def test_params4bit_real_serialization(device, quant_type, blocksize, compress_statistics):
     if device == "hpu" and not is_supported_on_hpu(quant_type):

tests/test_ops.py

@@ -152,7 +152,7 @@ class Test4bitBlockwiseQuantOps:
     @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32], ids=id_formatter("dtype"))
     @pytest.mark.parametrize("storage_dtype", [torch.uint8, torch.bfloat16], ids=id_formatter("storage_dtype"))
     @pytest.mark.parametrize("quant_type", ["fp4", "nf4"])
-    @pytest.mark.parametrize("blocksize", [64, 128, 256, 512] if not ROCM_WARP_SIZE_64 else [128, 256, 512])
+    @pytest.mark.parametrize("blocksize", [32, 64, 128, 256, 512] if not ROCM_WARP_SIZE_64 else [128, 256, 512])
     def test_quantize_4bit(self, device, dtype, storage_dtype, quant_type, blocksize):
         if device == "hpu" and not is_supported_on_hpu(quant_type, dtype, storage_dtype):
             pytest.skip("This configuration is not supported on HPU.")
@@ -176,7 +176,7 @@ def test_quantize_4bit(self, device, dtype, storage_dtype, quant_type, blocksize
     @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32], ids=id_formatter("dtype"))
     @pytest.mark.parametrize("storage_dtype", [torch.uint8, torch.bfloat16], ids=id_formatter("storage_dtype"))
     @pytest.mark.parametrize("quant_type", ["fp4", "nf4"])
-    @pytest.mark.parametrize("blocksize", [64, 128, 256, 512] if not ROCM_WARP_SIZE_64 else [128, 256, 512])
+    @pytest.mark.parametrize("blocksize", [32, 64, 128, 256, 512] if not ROCM_WARP_SIZE_64 else [128, 256, 512])
     def test_dequantize_4bit(self, device, dtype, storage_dtype, quant_type, blocksize):
         if device == "hpu" and not is_supported_on_hpu(quant_type, dtype, storage_dtype):
             pytest.skip("This configuration is not supported on HPU.")
@@ -210,7 +210,7 @@ def test_dequantize_4bit(self, device, dtype, storage_dtype, quant_type, blocksi
     @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32], ids=id_formatter("dtype"))
     @pytest.mark.parametrize("storage_dtype", [torch.uint8, torch.bfloat16], ids=id_formatter("storage_dtype"))
     @pytest.mark.parametrize("quant_type", ["fp4", "nf4"])
-    @pytest.mark.parametrize("blocksize", [64, 128, 256, 512] if not ROCM_WARP_SIZE_64 else [128, 256, 512])
+    @pytest.mark.parametrize("blocksize", [32, 64, 128, 256, 512] if not ROCM_WARP_SIZE_64 else [128, 256, 512])
     @pytest.mark.skipif(ROCM_WARP_SIZE_64, reason="this test is not supported on ROCm yet")
     def test_gemv_4bit(self, device, dtype, storage_dtype, quant_type, blocksize):
         if device == "hpu" and not is_supported_on_hpu(quant_type, dtype, storage_dtype):