Merge branch 'main' into add-aqt-docs

Author: msaroufim

dev-requirements.txt

@@ -12,3 +12,6 @@ pandas
 
 # Custom CUDA Extensions
 ninja
+
+# for FP6-LLM (can be removed once we remove fp16_to_fp6_original())
+qtorch

docs/source/api_ref_dtypes.rst

@@ -12,6 +12,8 @@ torchao.dtypes
 
     to_nf4
     UInt4Tensor
+    to_float6_e3m2
+    from_float6_e3m2
 
 ..
   _NF4Tensor - add after fixing torchao/dtypes/nf4tensor.py:docstring

setup.py

@@ -46,11 +46,12 @@ def get_extensions():
     use_cuda = torch.cuda.is_available() and CUDA_HOME is not None
     extension = CUDAExtension if use_cuda else CppExtension
 
-    extra_link_args = []
+    extra_link_args = ["-fopenmp"]
     extra_compile_args = {
         "cxx": [
             "-O3" if not debug_mode else "-O0",
             "-fdiagnostics-color=always",
+            "-fopenmp",
         ],
         "nvcc": [
             "-O3" if not debug_mode else "-O0",

test/dtypes/test_float6_e3m2.py

@@ -0,0 +1,127 @@
+import torch
+from torch.testing._internal.common_utils import (
+    TestCase,
+    instantiate_parametrized_tests,
+    parametrize,
+    run_tests,
+)
+from torchao.dtypes.float6_e3m2 import to_float6_e3m2, from_float6_e3m2
+
+
+_DTYPES = [torch.float32, torch.float16, torch.bfloat16]
+_DEVICES = ["cpu"] + (["cuda"] if torch.cuda.is_available() else [])
+
+
+class TestFp6(TestCase):
+
+    @parametrize("device", _DEVICES)
+    @parametrize("dtype", _DTYPES)
+    @parametrize(
+        "input_output",
+        [
+            (0.0,    0b000000),  # exact values
+            (1.0,    0b001100),  # normal numbers
+            (1.25,   0b001101),
+            (28.0,   0b011111),  # max
+            (0.1875, 0b000011),  # subnormal number
+            (0.0625, 0b000001),  # min
+            (29.0,   0b011111),  # normal round down
+            (26.0,   0b011110),  # normal round to nearest even
+            (0.1251, 0b000010),  # subnormal round down
+            (0.0314, 0b000001),  # subnormal round up
+            (0.03,   0b000000),  # underflow
+        ],
+    )
+    def test_to_float6_e3m2_no_bit_packing_correctness(self, device, dtype, input_output):
+        input, output = input_output
+        input = torch.tensor(input, device=device, dtype=dtype)
+        assert to_float6_e3m2(input, no_bit_packing=True).item() == output
+
+    @parametrize("device", _DEVICES)
+    @parametrize("dtype", _DTYPES)
+    def test_to_float6_e3m2_bit_packing_correctness(self, device, dtype):
+        x = torch.randn(128, 128, device=device, dtype=dtype)
+        results_unpacked = to_float6_e3m2(x, no_bit_packing=True)
+        results_packed = to_float6_e3m2(x)
+
+        val0, val1, val2, val3 = results_unpacked.unflatten(-1, (-1, 4)).unbind(-1)
+        bits0 = (val0 << 2) | (val1 >> 4)  # 0000 0011
+        bits1 = (val1 << 4) | (val2 >> 2)  # 1111 2222
+        bits2 = (val2 << 6) | (val3);      # 2233 3333
+
+        expected_packed = torch.stack([bits0, bits1, bits2], dim=-1).flatten(-2)
+        assert (results_packed == expected_packed).all()
+
+    @parametrize("device", _DEVICES)
+    @parametrize("shape", [(), (0,), (10,), (20, 20)])
+    def test_to_float6_e3m2_no_bit_packing_shape(self, device, shape):
+        x = torch.randn(shape, device=device)
+        result = to_float6_e3m2(x, no_bit_packing=True)
+        assert result.shape == shape
+
+    @parametrize("device", _DEVICES)
+    @parametrize("shape", [(4,), (20, 20)])
+    def test_to_float6_e3m2_bit_packing_shape(self, device, shape):
+        x = torch.randn(shape, device=device)
+        result = to_float6_e3m2(x)
+        assert result.shape == shape[:-1] + (shape[-1] // 4 * 3,)
+
+    @parametrize("device", _DEVICES)
+    @parametrize("dtype", _DTYPES)
+    @parametrize("no_bit_packing", [False, True])
+    def test_to_float6_e3m2_compile(self, device, dtype, no_bit_packing):
+        x = torch.randn(20, 20, device=device, dtype=dtype)
+        expected = to_float6_e3m2(x, no_bit_packing=no_bit_packing)
+
+        to_float6_e3m2_compiled = torch.compile(to_float6_e3m2)
+        actual = to_float6_e3m2_compiled(x, no_bit_packing=no_bit_packing)
+        torch.testing.assert_close(actual, expected)
+
+    @parametrize("device", _DEVICES)
+    @parametrize(
+        "input_output",
+        [
+            (0b000000, 0.0),
+            (0b001100, 1.0),
+            (0b011111, 28.0),    # max
+            (0b000001, 0.0625),  # min
+            (0b001110, 1.5),
+            (0b000011, 0.1875),  # subnormal
+        ],
+    )
+    def test_from_float6_e3m2_no_bit_packing_correctness(self, device, input_output):
+        input, output = input_output
+        input = torch.tensor(input, device=device, dtype=torch.uint8)
+        assert from_float6_e3m2(input, no_bit_packing=True).item() == output
+
+    @parametrize("device", _DEVICES)
+    def test_from_float6_e3m2_bit_packing_correctness(self, device):
+        x = torch.randint(256, (128, 128 // 4 * 3), device=device, dtype=torch.uint8)
+        actual = from_float6_e3m2(x)
+
+        bits0, bits1, bits2 = x.unflatten(-1, (-1, 3)).unbind(-1)
+        x_unpacked0 = bits0 >> 2
+        x_unpacked1 = ((bits0 & 0x3) << 4) | (bits1 >> 4)
+        x_unpacked2 = ((bits1 & 0xF) << 2) | (bits2 >> 6)
+        x_unpacked3 = bits2 & 0x3F
+
+        x_unpacked = torch.stack([x_unpacked0, x_unpacked1, x_unpacked2, x_unpacked3], dim=-1).flatten(-2)
+        expected = from_float6_e3m2(x_unpacked, no_bit_packing=True)
+        torch.testing.assert_close(actual, expected)
+
+    @parametrize("device", _DEVICES)
+    @parametrize("no_bit_packing", [False, True])
+    def test_from_float6_e3m2_compile(self, device, no_bit_packing):
+        x = torch.randint(256, size=(20, 15), device=device, dtype=torch.uint8)
+        expected = from_float6_e3m2(x, no_bit_packing=no_bit_packing)
+
+        from_float6_e3m2_compiled = torch.compile(from_float6_e3m2)
+        actual = from_float6_e3m2_compiled(x, no_bit_packing=no_bit_packing)
+        torch.testing.assert_close(actual, expected)
+
+
+instantiate_parametrized_tests(TestFp6)
+
+
+if __name__ == "__main__":
+    run_tests()

test/test_ops.py

@@ -50,24 +50,21 @@ def test_prepack_fp6_weight(self):
         opcheck(torch.ops.torchao.prepack_fp6_weight, (fp6_weight,), test_utils=test_utils)
 
     @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_fp16_to_fp6(self):
+    def test_fp16_to_fp6_original(self):
         OC = 256
         IC = 256
-
-        # in this fp6, we use 3 bits for exponent and 2 bits for mantissa
-        # also, we don't have nan/inf
-        fp6_absmax = 28.0  # 2 ** (0b111 - 0b011) * (1 + 0.5 + 0.25), where E=111, M=11
-        fp6_absmin = 0.0625  # 2 ** (-0b010) * 0.25, where E=000, M=01 (subnormal number)
         fp16_weight = torch.randn((OC, IC), dtype=torch.float16)
-        fp16_weight.clip_(-fp6_absmax, fp6_absmax)
-        fp16_weight[fp16_weight.abs() < fp6_absmin] = 0
+
+        # the original FP16->FP6 kernel checks for overflow/underflow
+        fp16_weight.clip_(-28.0, 28.0)
+        fp16_weight[fp16_weight.abs() < 0.0625] = 0.0
 
         # smoke test
-        torchao.ops.fp16_to_fp6(fp16_weight)
+        torchao.ops.fp16_to_fp6_original(fp16_weight)
 
         # comprehensive testing
         test_utils = ["test_schema", "test_autograd_registration", "test_faketensor", "test_aot_dispatch_dynamic"]
-        opcheck(torch.ops.torchao.fp16_to_fp6, (fp16_weight,), test_utils=test_utils)
+        opcheck(torch.ops.torchao.fp16_to_fp6_original, (fp16_weight,), test_utils=test_utils)
 
     @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
     def test_fp16act_fp6weight_linear(self):
@@ -89,19 +86,6 @@ def test_fp16act_fp6weight_linear(self):
         test_utils = ["test_schema", "test_autograd_registration", "test_faketensor", "test_aot_dispatch_dynamic"]
         opcheck(torch.ops.torchao.fp16act_fp6weight_linear, (act_cuda, weight_cuda, scale_cuda, splitK), test_utils=test_utils)
 
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_fp6_weight_dequant(self):
-        OC = 256
-        IC = 256
-        fp6_weight, fp16_scale, _ = self._create_fp6_inputs(0, OC, IC)
-
-        # smoke test
-        torchao.ops.fp6_weight_dequant(fp6_weight, fp16_scale)
-
-        # comprehensive testing
-        test_utils = ["test_schema", "test_autograd_registration", "test_faketensor", "test_aot_dispatch_dynamic"]
-        opcheck(torch.ops.torchao.fp6_weight_dequant, (fp6_weight, fp16_scale), test_utils=test_utils)
-
     # adapted from https://github.com/usyd-fsalab/fp6_llm/blob/main/tests/python/kernel_test.py
     @parameterized.expand([(1, 2048, 4096, 5), (2, 8192, 8192, 6)])
     @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
@@ -115,8 +99,8 @@ def test_fp6_matmul_correctness(self, BS, OC, IC, splitK):
 
         results_fp6 = torchao.ops.fp16act_fp6weight_linear(act_cuda, weight_cuda, scale_cuda, splitK)
 
-        fp16_weight = torchao.ops.fp6_weight_dequant(fp6_weight, fp16_scale).cuda()
-        results_fp16 = act_cuda @ fp16_weight.T
+        fp16_weight = torchao.dtypes.from_float6_e3m2(fp6_weight.view(torch.uint8), dtype=torch.float16) * fp16_scale[:, None]
+        results_fp16 = act_cuda @ fp16_weight.cuda().T
 
         error = (results_fp6 - results_fp16).abs()
         relative_error = error / results_fp16.abs()

torchao/__init__.py

@@ -1,9 +1,3 @@
-from torchao.quantization import (
-    apply_weight_only_int8_quant,
-    apply_dynamic_quant,
-    autoquant,
-)
-from . import dtypes
 import torch
 _IS_FBCODE = (
     hasattr(torch._utils_internal, "IS_FBSOURCE") and
@@ -14,6 +8,13 @@
     from . import _C
     from . import ops
 
+from torchao.quantization import (
+    apply_weight_only_int8_quant,
+    apply_dynamic_quant,
+    autoquant,
+)
+from . import dtypes
+
 __all__ = [
     "dtypes",
     "apply_dynamic_quant",

torchao/csrc/cuda/fp6_llm/weight_quant.cu

@@ -13,7 +13,6 @@
 //    limitations under the License.
 // 
 // This file is adapted from https://github.com/usyd-fsalab/fp6_llm/blob/ce76774bcfc26b325c1b558abcf1935026d9abbc/fp6_llm/csrc/utils/weight_quant.h
-// and https://github.com/usyd-fsalab/fp6_llm/blob/ce76774bcfc26b325c1b558abcf1935026d9abbc/fp6_llm/csrc/utils/weight_dequant.h
 
 #include <cuda_fp16.h>
 #include <iostream>
@@ -120,49 +119,14 @@ void weight_prepacking_fp16_to_fp6(uint16_t* weight_16bit,
     }
 }
 
-void DeQuantMatrix_FP6_To_FP16(half* A_16bit_h, unsigned char* A_6bit_h, size_t M, size_t K, half* scale) {
-    assert(M%64==0);                 // Currently, M must be a multiple of 64.
-    assert(K%64==0);                 // Currently, K must be a multiple of 64.
-    size_t TotalSizeInByte = M*K*6/8;
-    //
-    half* OutPTR = A_16bit_h;
-    for(size_t i=0; i<TotalSizeInByte/3; i++) {    // 4 FP6 = 3 Bytes for each Loop
-        unsigned char   B1  = A_6bit_h[i*3+0] & 0xfc;
-                        B1  = (B1&0x80) | ((B1>>2)&0x1f);
-        unsigned char   B2  = (A_6bit_h[i*3+0]<<6) | ((A_6bit_h[i*3+1]>>2)&0xfc);
-                        B2  = (B2&0x80) | ((B2>>2)&0x1f);
-        unsigned char   B3  = (A_6bit_h[i*3+1]<<4) | ((A_6bit_h[i*3+2]>>4)&0xfc);
-                        B3  = (B3&0x80) | ((B3>>2)&0x1f);
-        unsigned char   B4  = A_6bit_h[i*3+2]<<2;
-                        B4  = (B4&0x80) | ((B4>>2)&0x1f);
-        half            FP1, FP2, FP3, FP4;
-        unsigned char   *PTR1, *PTR2, *PTR3, *PTR4;
-        PTR1 = reinterpret_cast<unsigned char*>(&FP1);
-        PTR2 = reinterpret_cast<unsigned char*>(&FP2);
-        PTR3 = reinterpret_cast<unsigned char*>(&FP3);
-        PTR4 = reinterpret_cast<unsigned char*>(&FP4);
-        PTR1[0] = 0;    PTR1[1] = B1;   // small endian for X86 CPU
-        PTR2[0] = 0;    PTR2[1] = B2;
-        PTR3[0] = 0;    PTR3[1] = B3;
-        PTR4[0] = 0;    PTR4[1] = B4;
-        OutPTR[0] = __float2half_rn ( __half2float(FP1) * 4096.0f * __half2float(scale[(4*i)/K]) );
-        OutPTR[1] = __float2half_rn ( __half2float(FP2) * 4096.0f * __half2float(scale[(4*i)/K]) );
-        OutPTR[2] = __float2half_rn ( __half2float(FP3) * 4096.0f * __half2float(scale[(4*i)/K]) );
-        OutPTR[3] = __float2half_rn ( __half2float(FP4) * 4096.0f * __half2float(scale[(4*i)/K]) );
-        //
-        OutPTR +=4;
-    }
-}
-
-
 #include <torch/extension.h>
 #include <ATen/ATen.h>
 #include <torch/library.h>
 
 namespace torchao {
 
 // https://github.com/microsoft/DeepSpeed/blob/0fc19b6a320cf8aa0a5f6c2b1fa310bae9a70d94/deepspeed/inference/v2/kernels/core_ops/cuda_linear/linear_kernels.cpp#L194
-at::Tensor fp16_to_fp6_cpu(at::Tensor fp16_tensor)
+at::Tensor fp16_to_fp6_original_cpu(at::Tensor fp16_tensor)
 {
     TORCH_CHECK(fp16_tensor.dim() == 2, "weight must be 2-dimensional");
     TORCH_CHECK(fp16_tensor.scalar_type() == torch::kFloat16, "weight must be FP16");
@@ -183,37 +147,8 @@ at::Tensor fp16_to_fp6_cpu(at::Tensor fp16_tensor)
     return packed_fp6_tensor;
 }
 
-/*
- * Dequant a FP6 matrix to a equivalent FP16 matrix using CPUs.
- * A useful tool to construct input matrices for the FP16 GEMM baseline.
- * [Input]
- *  fp6_tensor:  int  tensor of shape [OC, IC // 16 * 3];   // 3 INT32 words contains 16 FP6  weights.
- *  fp16_scale:  half tensor of shape [OC];                 // for row-wise quantization.
- * [Output]
- *  fp16_tensor: half tensor of shape [OC, IC].     
- */
-at::Tensor weight_matrix_dequant_cpu(at::Tensor fp6_tensor, at::Tensor fp16_scale) 
-{
-    int OC = fp6_tensor.size(0);
-    TORCH_CHECK(fp6_tensor.size(1) % 3 == 0);
-    int IC = fp6_tensor.size(1) / 3 * 16;
-    TORCH_CHECK(fp16_scale.size(0) == OC);
-    //
-    auto fp6_tensor_ptr = reinterpret_cast<unsigned char*>(fp6_tensor.data_ptr<int>());
-    auto fp16_scale_ptr = reinterpret_cast<half*>(fp16_scale.data_ptr<at::Half>());
-    //
-    auto options = at::TensorOptions().dtype(at::kHalf).device(fp16_scale.device());
-    at::Tensor fp16_tensor = at::empty({OC, IC}, options);
-    auto fp16_tensor_ptr = reinterpret_cast<half*>(fp16_tensor.data_ptr<at::Half>());
-    //
-    DeQuantMatrix_FP6_To_FP16(fp16_tensor_ptr, fp6_tensor_ptr, OC, IC, fp16_scale_ptr);
-    //
-    return fp16_tensor;
-}
-
 TORCH_LIBRARY_IMPL(torchao, CPU, m) {
-  m.impl("torchao::fp16_to_fp6", &fp16_to_fp6_cpu);
-  m.impl("torchao::fp6_weight_dequant", &weight_matrix_dequant_cpu);
+  m.impl("torchao::fp16_to_fp6_original", &fp16_to_fp6_original_cpu);
 }
 
 }