Add CUTLASS-based W4A4

benchmarks/benchmark_rowwise_scaled_linear_cutlass.py

@@ -0,0 +1,70 @@
+import pandas as pd
+import torch
+from tqdm import tqdm
+from triton.testing import do_bench
+
+from torchao.ops import (
+    rowwise_scaled_linear_cutlass_s4s4,
+    rowwise_scaled_linear_cutlass_s8s4,
+)
+
+
+def benchmark_microseconds(f, *args):
+    return do_bench(lambda: f(*args), return_mode="median") * 1e3
+
+
+def get_problem(m: int, n: int, k: int, A_nbits: int, B_nbits: int):
+    assert A_nbits in (4, 8) and B_nbits in (4, 8)
+
+    dev = torch.device("cuda")
+    A = torch.randint(-128, 127, (m, k * A_nbits // 8), dtype=torch.int8, device=dev)
+    A_scale = torch.randn((m,), dtype=torch.half, device=dev)
+    B = torch.randint(
+        -128, 127, size=(n, k * B_nbits // 8), dtype=torch.int8, device=dev
+    )
+    B_scale = torch.randn((n,), dtype=torch.half, device=dev)
+    C = None
+
+    return A, A_scale, B, B_scale, C
+
+
+def benchmark(m: int, k: int, n: int):
+    dev = torch.device("cuda")
+    A_ref = torch.randn((m, k), dtype=torch.half, device=dev)
+    B_ref = torch.randn((n, k), dtype=torch.half, device=dev)
+    fp16_time = benchmark_microseconds(torch.nn.functional.linear, A_ref, B_ref)
+
+    A, A_scale, B, B_scale, C = get_problem(m, n, k, 8, 4)
+    rowwise_scaled_linear_cutlass_s8s4_time = benchmark_microseconds(
+        rowwise_scaled_linear_cutlass_s8s4, A, A_scale, B, B_scale, C
+    )
+
+    A, A_scale, B, B_scale, C = get_problem(m, n, k, 4, 4)
+    rowwise_scaled_linear_cutlass_s4s4_time = benchmark_microseconds(
+        rowwise_scaled_linear_cutlass_s4s4, A, A_scale, B, B_scale, C
+    )
+
+    return {
+        "m": m,
+        "k": k,
+        "n": n,
+        "fp16_latency (ms)": fp16_time,
+        "rowwise_scaled_linear_cutlass_s8s4 latency (ms)": rowwise_scaled_linear_cutlass_s8s4_time,
+        "s8s4 speedup (d/s)": fp16_time / rowwise_scaled_linear_cutlass_s8s4_time,
+        "rowwise_scaled_linear_cutlass_s4s4 latency (ms)": rowwise_scaled_linear_cutlass_s4s4_time,
+        "s4s4 speedup (d/s)": fp16_time / rowwise_scaled_linear_cutlass_s4s4_time,
+    }
+
+
+if __name__ == "__main__":
+    k_vals = (8192, 8192, 8192, 28672)
+    n_vals = (8192, 10240, 57344, 8192)
+
+    results = []
+    for m in tqdm([1 << i for i in range(10)]):
+        for n, k in zip(n_vals, k_vals):
+            results.append(benchmark(m, k, n))
+
+    df = pd.DataFrame(results)
+    df.to_csv("rowwise_scaled_linear_cutlass_time_results.csv", index=False)
+    print(df.to_markdown(index=False))

setup.py

@@ -240,30 +240,42 @@ def get_extensions():
             extra_compile_args["nvcc"].append("-g")
             extra_link_args.append("/DEBUG")
 
+    this_dir = os.path.dirname(os.path.curdir)
+    extensions_dir = os.path.join(this_dir, "torchao", "csrc")
+    sources = list(glob.glob(os.path.join(extensions_dir, "**/*.cpp"), recursive=True))
+
+    extensions_cuda_dir = os.path.join(extensions_dir, "cuda")
+    cuda_sources = list(
+        glob.glob(os.path.join(extensions_cuda_dir, "**/*.cu"), recursive=True)
+    )
+
+    if use_cuda:
+        sources += cuda_sources
+
     use_cutlass = False
     if use_cuda and not IS_WINDOWS:
         use_cutlass = True
         cutlass_dir = os.path.join(third_party_path, "cutlass")
         cutlass_include_dir = os.path.join(cutlass_dir, "include")
+        cutlass_extensions_include_dir = os.path.join(cwd, extensions_cuda_dir)
     if use_cutlass:
         extra_compile_args["nvcc"].extend(
             [
                 "-DTORCHAO_USE_CUTLASS",
                 "-I" + cutlass_include_dir,
+                "-I" + cutlass_extensions_include_dir,
             ]
         )
-
-    this_dir = os.path.dirname(os.path.curdir)
-    extensions_dir = os.path.join(this_dir, "torchao", "csrc")
-    sources = list(glob.glob(os.path.join(extensions_dir, "**/*.cpp"), recursive=True))
-
-    extensions_cuda_dir = os.path.join(extensions_dir, "cuda")
-    cuda_sources = list(
-        glob.glob(os.path.join(extensions_cuda_dir, "**/*.cu"), recursive=True)
-    )
-
-    if use_cuda:
-        sources += cuda_sources
+    else:
+        # Remove CUTLASS-based kernels from the cuda_sources list.  An
+        # assumption is that these files will have "cutlass" in its
+        # name.
+        cutlass_sources = list(
+            glob.glob(
+                os.path.join(extensions_cuda_dir, "**/*cutlass*.cu"), recursive=True
+            )
+        )
+        sources = [s for s in sources if s not in cutlass_sources]
 
     ext_modules = []
     if len(sources) > 0:

test/dtypes/test_affine_quantized.py

@@ -11,6 +11,7 @@
 from torchao.dtypes import CutlassInt4PackedLayout, Int4CPULayout, SemiSparseLayout
 from torchao.quantization import (
     float8_weight_only,
+    int4_dynamic_activation_int4_weight,
     int4_weight_only,
     int8_dynamic_activation_int4_weight,
     int8_dynamic_activation_int8_weight,
@@ -61,6 +62,7 @@ def get_quantization_functions(
                         layout=CutlassInt4PackedLayout(),
                     )
                 )
+                base_functions.append(int4_dynamic_activation_int4_weight())
 
     if do_sparse:
         base_functions.append(

test/test_rowwise_scaled_linear_cutlass.py

@@ -0,0 +1,104 @@
+import itertools
+
+import pytest
+import torch
+
+from torchao.ops import (
+    rowwise_scaled_linear_cutlass_s4s4,
+    rowwise_scaled_linear_cutlass_s8s4,
+)
+from torchao.quantization.utils import group_quantize_tensor_symmetric
+
+ROWWISE_SCALED_LINEAR_CUTLASS_DTYPE = [torch.float16, torch.bfloat16]
+ROWWISE_SCALED_LINEAR_CUTLASS_BATCH_SIZE = [1, 4, 8, 16, 32, 64]
+ROWWISE_SCALED_LINEAR_CUTLASS_SIZE_MNK = [
+    (2, 512, 128),
+    (3, 2048, 2048),
+    (4, 3584, 640),
+    (13, 8704, 8576),
+    (26, 18944, 1664),
+    (67, 6656, 1408),
+]
+ROWWISE_SCALED_LINEAR_CUTLASS_USE_BIAS = [False, True]
+ROWWISE_SCALED_LINEAR_CUTLASS_TEST_PARAMS = list(
+    itertools.product(
+        ROWWISE_SCALED_LINEAR_CUTLASS_DTYPE,
+        ROWWISE_SCALED_LINEAR_CUTLASS_BATCH_SIZE,
+        ROWWISE_SCALED_LINEAR_CUTLASS_SIZE_MNK,
+        ROWWISE_SCALED_LINEAR_CUTLASS_USE_BIAS,
+    )
+)
+
+
+def run_test_for_op(op, xq_bits, wq_bits, dtype, batch_size, size_mnk, use_bias):
+    assert xq_bits in [4, 8]
+    assert wq_bits in [4, 8]
+
+    size_m, size_n, size_k = size_mnk
+
+    x = torch.randn((batch_size, size_m, size_k), dtype=dtype, device="cuda")
+    w = torch.rand((size_n, size_k), dtype=dtype, device="cuda")
+    bias = torch.rand((size_n,), dtype=dtype, device="cuda") if use_bias else None
+
+    x_2d = x.view(-1, x.shape[-1])
+    xq_2d_s8, xq_2d_scales, xq_2d_zeros = group_quantize_tensor_symmetric(
+        x_2d, xq_bits, size_k, dtype
+    )
+    assert torch.all(xq_2d_zeros == 0)
+    xq_s8 = xq_2d_s8.reshape(x.shape)
+    if xq_bits == 4:
+        xq = (xq_s8[..., 1::2] << 4) | (xq_s8[..., 0::2] & 0xF)
+    else:
+        xq = xq_s8
+    xq_scales = xq_2d_scales.reshape(x.shape[:-1])
+
+    wq_s8, wq_scales, wq_zeros = group_quantize_tensor_symmetric(
+        w, wq_bits, size_n, dtype
+    )
+    assert torch.all(wq_zeros == 0)
+    if wq_bits == 4:
+        wq = (wq_s8[:, 1::2] << 4) | (wq_s8[:, 0::2] & 0xF)
+    else:
+        wq = wq_s8
+
+    # If torch.nn.functional.linear(x, w, bias) used as reference, the
+    # error would be too big.  The calculation below is approximately
+    # what rowwise_scaled_linear_cutlass kernel is doing (except that
+    # matrix multiplication is over integers there).
+    size_m_2d = x_2d.shape[0]
+    output_ref = (
+        (xq_2d_s8.float() @ wq_s8.float().T)
+        * xq_2d_scales.view(size_m_2d, 1)
+        * wq_scales.view(1, size_n)
+    )
+    if bias is not None:
+        output_ref += bias
+    output_ref = output_ref.to(dtype).reshape(x.shape[:-1] + (size_n,))
+
+    fn_inputs = (xq, xq_scales, wq, wq_scales, bias)
+    try:
+        output = op(*fn_inputs)
+    except NotImplementedError:
+        pytest.xfail("operator not implemented")
+
+    torch.testing.assert_close(output, output_ref)
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.parametrize(
+    "dtype, batch_size, size_mnk, use_bias", ROWWISE_SCALED_LINEAR_CUTLASS_TEST_PARAMS
+)
+def test_rowwise_scaled_linear_cutlass_s4s4(dtype, batch_size, size_mnk, use_bias):
+    run_test_for_op(
+        rowwise_scaled_linear_cutlass_s4s4, 4, 4, dtype, batch_size, size_mnk, use_bias
+    )
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.parametrize(
+    "dtype, batch_size, size_mnk, use_bias", ROWWISE_SCALED_LINEAR_CUTLASS_TEST_PARAMS
+)
+def test_rowwise_scaled_linear_cutlass_s8s4(dtype, batch_size, size_mnk, use_bias):
+    run_test_for_op(
+        rowwise_scaled_linear_cutlass_s8s4, 8, 4, dtype, batch_size, size_mnk, use_bias
+    )

torchao/csrc/README.md

@@ -8,7 +8,6 @@ The goal is that you can focus on just writing your custom CUDA or C++ kernel an
 
 To learn more about custom ops in PyTorch you can refer to the [PyTorch Custom Operators Landing Page](https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html)
 
-
 ## How to add your own kernel in ao
 
 We've integrated several kernels which you can use as a template for your own kernels. `tensor_core_tiled_layout` is the most straight-forward to get started with.
@@ -23,6 +22,8 @@ And that's it! Once CI passes and your code merged you'll be able to point peopl
 
 If you'd like to learn more please check out [torch.library](https://pytorch.org/docs/main/library.html)
 
+Note: All CUTLASS-based kernels should have `cutlass` in the name of their `.cu` files e.g. `rowwise_scaled_linear_cutlass_s4s4.cu`
+
 ## Required dependencies
 
 The important dependencies are already taken care of in our CI so feel free to test in CI directly