Port oss f16_fast_gemv into fbcode

fbgemm_gpu/experimental/gen_ai/CMakeLists.txt

@@ -33,7 +33,10 @@ endif()
 # CUDA-specific sources
 file(GLOB_RECURSE experimental_gen_ai_cpp_source_files_cuda
   src/quantize/cutlass_extensions/*.cu
-  src/quantize/cutlass_extensions/**/*.cu)
+  src/quantize/cutlass_extensions/**/*.cu
+  src/quantize/fast_gemv/*.cu
+  src/quantize/fast_gemv/**/*.cu
+  src/quantize/fast_gemv/**/*.cuh)
 
 # HIP-specific sources
 file(GLOB_RECURSE experimental_gen_ai_cpp_source_files_hip

fbgemm_gpu/experimental/gen_ai/bench/quantize_ops.py

@@ -370,6 +370,38 @@ def cuda(self) -> bool:
         return True
 
 
+@register_quantize_op
+class BF16OSSFastGemv(QuantizeOpBase):
+    """
+    BF16 OSS fast gemv kernel.
+    """
+
+    def quantize(self, x, w):
+        # dummy quantize
+        return x, w
+
+    def compute(self, x, w):
+        out = torch.ops.fbgemm.bf16_fast_gemv(x, w)
+        return out
+
+    def quantize_and_compute(self, x, w):
+        x, w = self.quantize(x, w)
+        return self.compute(x, w)
+
+    @property
+    def name(self) -> str:
+        return "bf16_oss_fast_gemv"
+
+    @property
+    def hip(self) -> bool:
+        # This implementation is specific to cublas.
+        return False
+
+    @property
+    def cuda(self) -> bool:
+        return True
+
+
 @register_quantize_op
 class FP8CublasRowwiseGemm(QuantizeOpBase):
     """

fbgemm_gpu/experimental/gen_ai/src/quantize/fast_gemv/bf16_fast_gemv.cu

@@ -0,0 +1,138 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/core/ScalarType.h>
+#include <c10/cuda/CUDAGuard.h>
+#include <cuda_bf16.h>
+
+#include "include/fast_gemv.cuh"
+
+namespace fbgemm_gpu {
+
+// The heuristics are derived by sweeping over 4 different
+// problem sizes we care about and selected the best elapsed time/bw
+// combination. See more in
+// deeplearning/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/fast_gemv/sweep_utils.py
+dim3 get_best_block_dim(int m, int n, int k) {
+  if (m == 1 && n == 1280 && k == 8192) {
+    return dim3(128, 4);
+  } else if (m == 1 && n == 8192 && k == 1024) {
+    return dim3(32, 8);
+  } else if (m == 1 && n == 7168 && k == 8192) {
+    return dim3(256, 1);
+  } else if (m == 1 && n == 8192 && k == 3584) {
+    return dim3(64, 2);
+  } else {
+    // Default block dimensions
+    return dim3(32, 4);
+  }
+}
+
+at::Tensor bf16_fast_gemv(at::Tensor X, at::Tensor W) {
+  // X: M x K
+  // W: N x K
+  auto m = X.size(0);
+  auto n = W.size(0);
+  auto k = W.size(1);
+
+  TORCH_CHECK(X.is_cuda() && X.is_contiguous());
+  TORCH_CHECK(W.is_cuda() && W.is_contiguous());
+
+  dim3 block_dim = get_best_block_dim(m, n, k);
+
+  TORCH_CHECK(
+      n % block_dim.y == 0,
+      "Invalid block dimensions: n (",
+      n,
+      ") must be divisible by block_dim.y (",
+      block_dim.y,
+      "). Received n: ",
+      n,
+      ", block_dim.y: ",
+      block_dim.y,
+      " Please either use a `n` which is divisible by `block_dim.y`, or update "
+      "`get_best_block_dim()` heuristics to choose another `block_dim.y`. "
+      " All current params - m: ",
+      m,
+      ", n: ",
+      n,
+      ", k: ",
+      k,
+      ", block_dim.x: ",
+      block_dim.x,
+      ", block_dim.y: ",
+      block_dim.y,
+      ".");
+  TORCH_CHECK(
+      k % block_dim.x == 0,
+      "Invalid block dimensions: k (",
+      k,
+      ") must be divisible by block_dim.x (",
+      block_dim.x,
+      "). Received k: ",
+      k,
+      ", block_dim.x: ",
+      block_dim.x,
+      " Please either use a `k` which is divisible by `block_dim.x`, or update "
+      "`get_best_block_dim()` heuristics to choose another `block_dim.x`."
+      " All current params - m: ",
+      m,
+      ", n: ",
+      n,
+      ", k: ",
+      k,
+      ", block_dim.x: ",
+      block_dim.x,
+      ", block_dim.y: ",
+      block_dim.y,
+      ".");
+  TORCH_CHECK(
+      (k / block_dim.x) % 8 == 0,
+      "Invalid num_per_thread: (",
+      k / block_dim.x,
+      ") must be divisible by 8.",
+      " Received k: ",
+      k,
+      ", block_dim.x: ",
+      block_dim.x,
+      " Please either use a `k` that `k / block_dim.x` that is divisble by 8, or update "
+      "`get_best_block_dim()` heuristics to choose another `block_dim.x`."
+      " All current params - m: ",
+      m,
+      ", n: ",
+      n,
+      ", k: ",
+      k,
+      ", block_dim.x: ",
+      block_dim.x,
+      ", block_dim.y: ",
+      block_dim.y,
+      ".");
+
+  dim3 grid_dim(1, n / block_dim.y);
+  unsigned int num_per_thread = k / block_dim.x;
+
+  auto stream = at::cuda::getCurrentCUDAStream();
+
+  auto Y = at::empty({m, n}, X.options().dtype(at::kBFloat16));
+
+  gemv_bf16<<<grid_dim, block_dim, 0, stream>>>(
+      reinterpret_cast<__nv_bfloat16*>(W.data_ptr()), // mat
+      reinterpret_cast<__nv_bfloat16*>(X.data_ptr()), // vec
+      reinterpret_cast<__nv_bfloat16*>(Y.data_ptr()), // res
+      k,
+      num_per_thread);
+
+  C10_CUDA_KERNEL_LAUNCH_CHECK();
+
+  return Y;
+}
+
+} // namespace fbgemm_gpu