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https://github.com/ml-explore/mlx.git
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Add gemv masked to JIT plus some fixes (#1310)
* add gemv masked to JIT plus some fixes * some cleanup * add utils * fix * fix 2 * more cleaning * fix * remove unused mps matmul support * one more nit * revert
This commit is contained in:
Awni Hannun
@@ -11,187 +11,14 @@
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#include "mlx/backend/metal/kernels/defines.h"
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#include "mlx/backend/metal/kernels/steel/gemm/params.h"
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#include "mlx/backend/metal/matmul.h"
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#include "mlx/backend/metal/mps/gemm.h"
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#include "mlx/backend/metal/utils.h"
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#include "mlx/primitives.h"
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#include "mlx/utils.h"
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namespace mlx::core {
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///////////////////////////////////////////////////////////////////////////////
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// MPS Matmul fallback
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///////////////////////////////////////////////////////////////////////////////
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namespace {
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bool use_mps() {
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auto get_val = []() {
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if (const char* buff_str = std::getenv("MLX_USE_MPS")) {
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return std::string(buff_str) != "OFF";
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} else {
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return false;
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}
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};
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static bool use_mps_ = get_val();
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return use_mps_;
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}
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#define MAX_OPS_PER_BUFFER max_ops_per_buffer()
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inline void mps_matmul(
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const Stream& s,
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metal::Device& d,
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const array& a,
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const array& b,
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array& out,
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int M,
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int N,
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int K,
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int batch_size_out,
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int lda,
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int ldb,
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bool transpose_a,
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bool transpose_b,
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std::vector<array>& copies,
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float alpha = 1.0f,
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float beta = 0.0f) {
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MPS::DataType mps_dtype = MPS::DataTypeFloat32;
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if (out.dtype() == float16) {
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mps_dtype = MPS::DataTypeFloat16;
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} else if (out.dtype() == bfloat16) {
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mps_dtype = MPS::DataTypeBFloat16;
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}
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// Used batched MPSMatrixMultiplication if batch_size_out > 1
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// We only accept the following cases:
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// 1. Both a, b have batch_size_out matrices worth of data
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// 2. Only one of a or b has batch_size_out matrices worth of data and
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// the other has matrix worth of data
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// The matrix dimensions of a and b are sure to be regularly strided
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if (batch_size_out > 1) {
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// No broadcasting defaults
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auto batch_size_a = a.data_size() / (M * K);
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auto batch_size_b = b.data_size() / (K * N);
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auto matrix_stride_a = M * K;
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auto matrix_stride_b = K * N;
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auto matrix_stride_out = M * N;
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// At this point, batch_size_a, batch_size_b show the number of matrices
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// in data, no broadcasted strides considered
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if (batch_size_out == std::max(batch_size_a, batch_size_b)) {
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// Handle simple broadcasting
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if (std::min(batch_size_a, batch_size_b) == 1) {
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matrix_stride_a = (batch_size_a == 1) ? 0 : matrix_stride_a;
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matrix_stride_b = (batch_size_b == 1) ? 0 : matrix_stride_b;
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batch_size_a = batch_size_out;
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batch_size_b = batch_size_out;
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}
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// Only proceed if broadcasting between a and b is simple
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// At this point, batch_size_a, batch_size_b show the number of matrices
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// after broadcasting
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if (batch_size_a == batch_size_b) {
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auto a_desc = MPS::MatrixDescriptor::matrixDescriptor(
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(M * K) / lda,
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lda,
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batch_size_a,
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lda * a.itemsize(),
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(matrix_stride_a * a.itemsize()),
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mps_dtype);
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auto b_desc = MPS::MatrixDescriptor::matrixDescriptor(
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(K * N) / ldb,
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ldb,
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batch_size_b,
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ldb * b.itemsize(),
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(matrix_stride_b * b.itemsize()),
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mps_dtype);
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auto out_desc = MPS::MatrixDescriptor::matrixDescriptor(
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M,
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N,
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batch_size_out,
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N * out.itemsize(),
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matrix_stride_out * out.itemsize(),
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mps_dtype);
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auto a_buf = static_cast<const MTL::Buffer*>(a.buffer().ptr());
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auto a_mat = MPS::Matrix::alloc()->init(a_buf, a_desc);
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auto b_buf = static_cast<const MTL::Buffer*>(b.buffer().ptr());
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auto b_mat = MPS::Matrix::alloc()->init(b_buf, b_desc);
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auto out_buf = static_cast<MTL::Buffer*>(out.buffer().ptr());
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auto out_mat = MPS::Matrix::alloc()->init(out_buf, out_desc);
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auto kernel = MPS::MatrixMultiplication::alloc()->init(
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d.mtl_device(), transpose_a, transpose_b, M, N, K, alpha, beta);
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auto command_buffer = d.get_command_buffer(s.index);
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kernel->setBatchSize(batch_size_out);
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kernel->setBatchStart(0);
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kernel->encodeToCommandBuffer(command_buffer, a_mat, b_mat, out_mat);
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command_buffer->addCompletedHandler(
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[a_mat, b_mat, out_mat, kernel, copies](
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MTL::CommandBuffer*) mutable {
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a_mat->release();
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b_mat->release();
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out_mat->release();
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kernel->release();
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copies.clear();
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});
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return;
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}
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}
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}
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// Schedule as many calls to MPSMatrixMultiplication as needed otherwise
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auto a_desc = MPS::MatrixDescriptor::matrixDescriptor(
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a.data_size() / lda, lda, lda * a.itemsize(), mps_dtype);
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auto b_desc = MPS::MatrixDescriptor::matrixDescriptor(
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b.data_size() / ldb, ldb, ldb * b.itemsize(), mps_dtype);
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auto out_desc = MPS::MatrixDescriptor::matrixDescriptor(
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batch_size_out * M, N, N * out.itemsize(), mps_dtype);
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auto a_buf = static_cast<const MTL::Buffer*>(a.buffer().ptr());
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auto a_mat = MPS::Matrix::alloc()->init(a_buf, a_desc);
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auto b_buf = static_cast<const MTL::Buffer*>(b.buffer().ptr());
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auto b_mat = MPS::Matrix::alloc()->init(b_buf, b_desc);
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auto out_buf = static_cast<MTL::Buffer*>(out.buffer().ptr());
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auto out_mat = MPS::Matrix::alloc()->init(out_buf, out_desc);
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auto kernel = MPS::MatrixMultiplication::alloc()->init(
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d.mtl_device(), transpose_a, transpose_b, M, N, K, alpha, beta);
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auto command_buffer = d.get_command_buffer(s.index);
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for (int i = 0; i < batch_size_out; ++i) {
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auto a_row = elem_to_loc(M * K * i, a.shape(), a.strides()) / lda;
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auto b_row = elem_to_loc(K * N * i, b.shape(), b.strides()) / ldb;
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kernel->setLeftMatrixOrigin({a_row, 0, 0});
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kernel->setRightMatrixOrigin({b_row, 0, 0});
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kernel->setResultMatrixOrigin({i * static_cast<size_t>(M), 0, 0});
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kernel->encodeToCommandBuffer(command_buffer, a_mat, b_mat, out_mat);
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}
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command_buffer->addCompletedHandler(
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[a_mat, b_mat, out_mat, kernel, copies](MTL::CommandBuffer*) mutable {
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a_mat->release();
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b_mat->release();
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out_mat->release();
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kernel->release();
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copies.clear();
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});
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}
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inline auto collapse_batches(const array& a, const array& b) {
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// Get and check the shape for the batched dims
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std::vector<int> A_bshape{a.shape().begin(), a.shape().end() - 2};
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@@ -860,26 +687,6 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
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/////////////////////////////////////////////////////////////////////////////
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// Gemm specialization
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if (use_mps()) {
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d.end_encoding(s.index);
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return mps_matmul(
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s,
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d,
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a,
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b,
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out,
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M,
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N,
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K,
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batch_size_out,
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a_cols,
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b_cols,
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a_transposed,
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b_transposed,
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copies);
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}
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return steel_matmul(
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/* const Stream& s = */ s,
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/* metal::Device& d = */ d,
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@@ -1529,8 +1336,22 @@ void BlockMaskedMM::eval_gpu(const std::vector<array>& inputs, array& out) {
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kname << "_nc" << !contiguous_kernel;
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// Encode and dispatch kernel
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auto kernel = get_gemv_masked_kernel(
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d,
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kname.str(),
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out,
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has_out_mask ? std::optional<array>{inputs[2]} : std::nullopt,
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has_op_mask ? std::optional<array>{inputs.back()} : std::nullopt,
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transpose_mat,
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bm,
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bn,
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sm,
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sn,
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tm,
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tn,
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contiguous_kernel);
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auto& compute_encoder = d.get_command_encoder(s.index);
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auto kernel = d.get_kernel(kname.str());
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compute_encoder->setComputePipelineState(kernel);
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int n_tgp = (out_vector_len + n_out_per_tgp - 1) / n_out_per_tgp;
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