Back out "Enable fast FP8 GEMM for memory bound"

Summary:
Original commit changeset: fbf34e283e94

Original Phabricator Diff: D68193920

Differential Revision: D68351266

fbgemm_gpu/experimental/gen_ai/bench/quantize_ops.py

@@ -716,40 +716,6 @@ def cuda(self) -> bool:
         return True
 
 
-@register_quantize_op
-class FP8LiteGemm(QuantizeOpBase):
-    """
-    FP8 lite matmul for memory bound.
-    """
-
-    def quantize(self, x, w):
-        # Quantize both input tensors.
-        xq, x_scale = torch.ops.fbgemm.quantize_fp8_per_tensor(x)
-        wq, w_scale = torch.ops.fbgemm.quantize_fp8_per_tensor(w)
-        return xq, wq, x_scale, w_scale
-
-    def compute(self, xq, wq, x_scale, w_scale):
-        return torch.ops.fbgemm.f8f8bf16_lite(xq, wq, x_scale * w_scale)
-
-    def quantize_and_compute(self, x, w):
-        xq, wq, x_scale, w_scale = self.quantize(x, w)
-        return self.compute(xq, wq, x_scale * w_scale)
-
-    @property
-    def name(self) -> str:
-        return "cuda_lite"
-
-    @property
-    def hip(self) -> bool:
-        # Need to add support for better quantize kernel.
-        # Also may have an issue with cuda graphs.
-        return False
-
-    @property
-    def cuda(self) -> bool:
-        return True
-
-
 @register_quantize_op
 class TritonFP8RowwiseGemm(QuantizeOpBase):
     """

fbgemm_gpu/experimental/gen_ai/src/quantize/quantize.cpp

@@ -55,7 +55,6 @@ at::Tensor f8f8bf16_tensorwise(
     at::Tensor WQ,
     double scale,
     bool use_fast_accum = true);
-at::Tensor f8f8bf16_lite(at::Tensor XQ, at::Tensor WQ, at::Tensor scale);
 std::vector<at::Tensor> f8f8bf16_grouped(
     at::TensorList XQ,
     at::TensorList WQ,
@@ -188,7 +187,6 @@ TORCH_LIBRARY_FRAGMENT(fbgemm, m) {
       "f8i4bf16_rowwise(Tensor XQ, Tensor WQ, Tensor x_scale, Tensor w_scale, Tensor w_zp) -> Tensor");
   m.def(
       "f8f8bf16_grouped(Tensor[] XQ, Tensor[] WQ, Tensor[] scale, Tensor? zero_start_index_M=None, bool use_fast_accum=True) -> Tensor[]");
-  m.def("f8f8bf16_lite(Tensor XQ, Tensor WQ, Tensor scale) -> Tensor");
   m.def(
       "bf16i4bf16_rowwise(Tensor X, Tensor WQ, Tensor w_scale, Tensor w_zp) -> Tensor");
   m.def(
@@ -270,7 +268,6 @@ TORCH_LIBRARY_IMPL(fbgemm, CUDA, m) {
   m.impl("f8f8bf16", f8f8bf16);
   m.impl("f8f8bf16_cublas", f8f8bf16_cublas);
   m.impl("f8f8bf16_grouped", f8f8bf16_grouped);
-  m.impl("f8f8bf16_lite", f8f8bf16_lite);
   m.impl("f8i4bf16_rowwise", f8i4bf16_rowwise);
   m.impl("bf16i4bf16_rowwise_batched", bf16i4bf16_rowwise_batched);
   m.impl("bf16i4bf16_rowwise", bf16i4bf16_rowwise);
@@ -298,7 +295,6 @@ TORCH_LIBRARY_IMPL(fbgemm, CPU, m) {
   m.impl("f8f8bf16", f8f8bf16);
   m.impl("f8f8bf16_cublas", f8f8bf16_cublas);
   m.impl("f8f8bf16_grouped", f8f8bf16_grouped);
-  m.impl("f8f8bf16_lite", f8f8bf16_lite);
   m.impl("f8i4bf16_rowwise", f8i4bf16_rowwise);
   m.impl("bf16i4bf16_rowwise_batched", bf16i4bf16_rowwise_batched);
   m.impl("bf16i4bf16_rowwise", bf16i4bf16_rowwise);
@@ -419,13 +415,6 @@ at::Tensor f8f8bf16_tensorwise_meta(
   return Y;
 }
 
-at::Tensor f8f8bf16_lite_meta(at::Tensor X, at::Tensor W, at::Tensor scale) {
-  const at::SymInt M = X.sym_size(0);
-  const at::SymInt N = W.sym_size(0);
-  auto Y = at::empty_symint({M, N}, X.options().dtype(at::kBFloat16));
-  return Y;
-}
-
 at::Tensor f8i4bf16_rowwise_meta(
     at::Tensor XQ, // FP8
     at::Tensor WQ, // INT4
@@ -544,7 +533,6 @@ TORCH_LIBRARY_IMPL(fbgemm, Meta, m) {
   m.impl("bf16i4bf16_rowwise", bf16i4bf16_rowwise_meta);
   m.impl("bf16i4bf16_rowwise_batched", bf16i4bf16_rowwise_batched_meta);
   m.impl("f8f8bf16_grouped", f8f8bf16_grouped_meta);
-  m.impl("f8f8bf16_lite", f8f8bf16_lite_meta);
 #endif
 }
 

fbgemm_gpu/experimental/gen_ai/test/quantize/quantize_test.py

@@ -1110,30 +1110,6 @@ def test_quantize_zero_input(self, K) -> None:
         torch.testing.assert_close(w.shape, wq.shape)
         torch.testing.assert_close(w_scale.shape, w_scale_ref.shape)
 
-    @unittest.skipIf(torch.version.hip, "Skip on AMD: fp8 lite op is yet suported.")
-    @settings(deadline=None)
-    @given(
-        M=st.sampled_from([1, 5, 16]),
-        N=st.sampled_from([1024, 6144]),
-        K=st.sampled_from([512, 3584]),
-        CudaGraph=st.sampled_from([True, False]),
-    )
-    def test_fp8_lite_matmul(self, M: int, N: int, K: int, CudaGraph: bool) -> None:
-        x = torch.randn(size=(M, K), dtype=torch.bfloat16, device="cuda") * 0.1
-        w = torch.randn(size=(N, K), dtype=torch.bfloat16, device="cuda") * 0.01
-        xq, x_scale = torch.ops.fbgemm.quantize_fp8_per_tensor(x)
-        wq, w_scale = torch.ops.fbgemm.quantize_fp8_per_tensor(w)
-        if CudaGraph:
-            zq = torch.ops.fbgemm.f8f8bf16_lite(xq, wq, x_scale * w_scale)
-            g = torch.cuda.CUDAGraph()
-            with torch.cuda.graph(g):
-                zq = torch.ops.fbgemm.f8f8bf16_lite(xq, wq, x_scale * w_scale)
-            g.replay()
-        else:
-            zq = torch.ops.fbgemm.f8f8bf16_lite(xq, wq, x_scale * w_scale)
-        zq_ref = (x @ w.T).to(torch.bfloat16)
-        torch.testing.assert_close(zq, zq_ref, atol=9.0e-2, rtol=9.0e-2)
-
 
 if __name__ == "__main__":
     unittest.main()