patch bump (#2588)

mlx/backend/cuda/gemms/cublas_gemm.cpp

@@ -230,15 +230,20 @@ void CublasGemm::set_out(
       batch_stride);
 }
 
-void CublasGemm::set_bias(void* bias) {
+void CublasGemm::set_bias(cu::CommandEncoder& encoder, const array& bias) {
+  encoder.set_input_array(bias);
   cublasLtEpilogue_t epilogue = CUBLASLT_EPILOGUE_BIAS;
   CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
       matmul_desc_,
       CUBLASLT_MATMUL_DESC_EPILOGUE,
       &epilogue,
       sizeof(epilogue)));
+  auto* bias_ptr = bias.data<void>();
   CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
-      matmul_desc_, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias, sizeof(bias)));
+      matmul_desc_,
+      CUBLASLT_MATMUL_DESC_BIAS_POINTER,
+      &bias_ptr,
+      sizeof(bias_ptr)));
 }
 
 void CublasGemm::run(

mlx/backend/cuda/gemms/cublas_gemm.h

@@ -55,7 +55,7 @@ class CublasGemm {
       int32_t batch_count,
       int64_t batch_stride);
 
-  void set_bias(void* bias);
+  void set_bias(cu::CommandEncoder& encoder, const array& bias);
 
   void run(
       cu::CommandEncoder& encoder,

mlx/backend/cuda/matmul.cpp

@@ -41,7 +41,7 @@ void gemm_and_bias(
     array& out,
     const array& a,
     const array& b,
-    void* bias = nullptr,
+    const std::optional<array>& bias = std::nullopt,
     float alpha = 1.0f) {
   // Check and collapse batch dimensions
   auto [batch_shape, a_batch_strides, b_batch_strides] = collapse_batches(a, b);
@@ -93,7 +93,7 @@ void gemm_and_bias(
       a_batch_strides.back(),
       b_batch_strides.back());
   if (bias) {
-    gemm.set_bias(bias);
+    gemm.set_bias(encoder, *bias);
   }
   gemm.run(
       encoder, out, a, b, batch_shape, a_batch_strides, b_batch_strides, alpha);
@@ -171,7 +171,7 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
         out,
         a,
         b,
-        c.data<void>(),
+        c,
         alpha_);
     return;
   }

mlx/distributed/nccl/nccl_stub/CMakeLists.txt

@@ -8,6 +8,7 @@ file(
   "${CMAKE_CURRENT_BINARY_DIR}/nccl.h")
 
 add_library(nccl SHARED nccl_stubs.cpp)
+set_target_properties(nccl PROPERTIES SOVERSION 2)
 find_package(CUDAToolkit REQUIRED)
 target_include_directories(nccl PRIVATE ${CUDAToolkit_INCLUDE_DIRS})
 target_include_directories(nccl PRIVATE ${CMAKE_CURRENT_BINARY_DIR})

mlx/version.h

@@ -4,7 +4,7 @@
 
 #define MLX_VERSION_MAJOR 0
 #define MLX_VERSION_MINOR 29
-#define MLX_VERSION_PATCH 0
+#define MLX_VERSION_PATCH 1
 #define MLX_VERSION_NUMERIC \
   (100000 * MLX_VERSION_MAJOR + 1000 * MLX_VERSION_MINOR + MLX_VERSION_PATCH)
 

python/tests/test_blas.py

@@ -702,7 +702,7 @@ class TestBlas(mlx_tests.MLXTestCase):
             b = mx.ones((5, 5))
             out = mx.addmm(a, b, a, beta=beta, alpha=alpha)
             expected = beta * a + alpha * (b @ a)
-            self.assertTrue(mx.allclose(expected, out, atol=1e-5))
+            self.assertTrue(mx.allclose(expected, out))
 
             # Broadcast c
             a = mx.ones((5, 5))
@@ -710,7 +710,7 @@ class TestBlas(mlx_tests.MLXTestCase):
             c = mx.ones((1, 5))
             out = mx.addmm(c, a, b, beta=beta, alpha=alpha)
             expected = beta * c + alpha * (a @ b)
-            self.assertTrue(mx.allclose(expected, out, atol=1e-5))
+            self.assertTrue(mx.allclose(expected, out))
 
     def test_addmm_grad(self):
         def make_ref_addmm(alpha, beta):