mirror of
https://github.com/ml-explore/mlx.git
synced 2025-06-24 01:17:26 +08:00
Angelos Katharopoulos
GitHub
parent
4c1dfa58b7
commit
71de73a668
@ -25,7 +25,7 @@ option(MLX_METAL_JIT "Use JIT compilation for Metal kernels" OFF)
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option(BUILD_SHARED_LIBS "Build mlx as a shared library" OFF)
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if(NOT MLX_VERSION)
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set(MLX_VERSION 0.23.0)
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set(MLX_VERSION 0.23.1)
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endif()
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add_compile_definitions("MLX_VERSION=${MLX_VERSION}")
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@ -533,45 +533,6 @@ void implicit_gemm_conv_2D_general_gpu(
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compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
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}
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void winograd_conv_2D_fused_gpu(
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const Stream& s,
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metal::Device& d,
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const array& in,
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const array& wt,
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array out,
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const MLXConvParams<2>& conv_params,
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std::vector<array>& copies_w) {
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int O_c = conv_params.O;
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int C_c = conv_params.C;
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int N_tiles_n = conv_params.N;
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int N_tiles_h = (conv_params.oS[0] + 1) / 2;
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int N_tiles_w = (conv_params.oS[1] + 1) / 2;
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int N_tiles = N_tiles_n * N_tiles_h * N_tiles_w;
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int bc = 32;
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int wm = 4;
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int wn = 1;
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std::ostringstream kname;
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kname << "winograd_conv_2d_fused_" << type_to_name(out) << "_flip"
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<< conv_params.flip;
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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.set_compute_pipeline_state(kernel);
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compute_encoder.set_input_array(in, 0);
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compute_encoder.set_input_array(wt, 1);
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compute_encoder.set_output_array(out, 2);
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compute_encoder.set_bytes(conv_params, 3);
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MTL::Size group_dims = MTL::Size(8, 8, 2);
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MTL::Size grid_dims =
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MTL::Size(O_c / 8, (N_tiles_h * N_tiles_w) / 8, N_tiles_n);
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compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
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}
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void winograd_conv_2D_gpu(
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const Stream& s,
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metal::Device& d,
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@ -580,6 +541,67 @@ void winograd_conv_2D_gpu(
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array out,
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const MLXConvParams<2>& conv_params,
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std::vector<array>& copies_w) {
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Shape padded_shape = {
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conv_params.N,
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conv_params.iS[0] + 2 * conv_params.pad[0],
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conv_params.iS[1] + 2 * conv_params.pad[1],
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conv_params.C};
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padded_shape[1] = 6 * ((padded_shape[1] - 2 + 5) / 6) + 2;
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padded_shape[2] = 6 * ((padded_shape[2] - 2 + 5) / 6) + 2;
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array in_padded(std::move(padded_shape), in.dtype(), nullptr, {});
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// Fill with zeros
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array zero_arr = array(0, in.dtype());
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fill_gpu(zero_arr, in_padded, s);
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copies_w.push_back(zero_arr);
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// Pick input slice from padded
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size_t data_offset = conv_params.pad[0] * in_padded.strides()[1] +
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conv_params.pad[1] * in_padded.strides()[2];
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array in_padded_slice(in.shape(), in_padded.dtype(), nullptr, {});
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in_padded_slice.copy_shared_buffer(
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in_padded,
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in_padded.strides(),
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in_padded.flags(),
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in_padded_slice.size(),
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data_offset);
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// Copy input values into the slice
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copy_gpu_inplace(in, in_padded_slice, CopyType::GeneralGeneral, s);
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copies_w.push_back(in_padded_slice);
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copies_w.push_back(in_padded);
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MLXConvParams<2> conv_params_updated{
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/* const int N = */ static_cast<int>(in_padded.shape(0)),
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/* const int C = */ static_cast<int>(in_padded.shape(3)),
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/* const int O = */ static_cast<int>(wt.shape(0)),
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/* const int iS[NDIM] = */
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{static_cast<int>(in_padded.shape(1)),
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static_cast<int>(in_padded.shape(2))},
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/* const int wS[NDIM] = */
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{static_cast<int>(wt.shape(1)), static_cast<int>(wt.shape(2))},
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/* const int oS[NDIM] = */
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{static_cast<int>(out.shape(1)), static_cast<int>(out.shape(2))},
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/* const int str[NDIM] = */ {1, 1},
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/* const int pad[NDIM] = */ {0, 0},
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/* const int kdil[NDIM] = */ {1, 1},
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/* const int idil[NDIM] = */ {1, 1},
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/* const size_t in_strides[NDIM + 2] = */
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{in_padded.strides()[0],
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in_padded.strides()[1],
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in_padded.strides()[2],
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in_padded.strides()[3]},
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/* const size_t wt_strides[NDIM + 2] = */
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{wt.strides()[0], wt.strides()[1], wt.strides()[2], wt.strides()[3]},
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/* const size_t out_strides[NDIM + 2] = */
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{out.strides()[0], out.strides()[1], out.strides()[2], out.strides()[3]},
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/* const int groups = */ 1,
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/* const bool flip = */ false,
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};
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int O_c = conv_params.O;
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int C_c = conv_params.C;
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@ -598,7 +620,7 @@ void winograd_conv_2D_gpu(
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int bo = 4;
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std::ostringstream kname;
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kname << "winograd_conv_2d_weight_transform_" << type_to_name(out) << "_bc"
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<< bc << "_flip" << conv_params.flip;
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<< bc;
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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.set_compute_pipeline_state(kernel);
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@ -631,10 +653,10 @@ void winograd_conv_2D_gpu(
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auto kernel = d.get_kernel(kname.str());
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compute_encoder.set_compute_pipeline_state(kernel);
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compute_encoder.set_input_array(in, 0);
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compute_encoder.set_input_array(in_padded, 0);
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compute_encoder.set_output_array(inp_wg, 1);
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compute_encoder.set_bytes(conv_params, 2);
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compute_encoder.set_bytes(conv_params_updated, 2);
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MTL::Size group_dims = MTL::Size(32, wn, wm);
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MTL::Size grid_dims = MTL::Size(N_tiles_w, N_tiles_h, N_tiles_n);
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@ -681,7 +703,7 @@ void winograd_conv_2D_gpu(
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compute_encoder.set_input_array(out_wg, 0);
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compute_encoder.set_output_array(out, 1);
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compute_encoder.set_bytes(conv_params, 2);
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compute_encoder.set_bytes(conv_params_updated, 2);
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MTL::Size group_dims = MTL::Size(32, wn, wm);
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MTL::Size grid_dims = MTL::Size(N_tiles_w, N_tiles_h, N_tiles_n);
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@ -745,18 +767,14 @@ void conv_2D_gpu(
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}
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// Direct to winograd conv
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bool img_large =
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bool inp_large =
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(conv_params.N * conv_params.iS[0] * conv_params.iS[1]) >= 1ul << 12;
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bool channels_large = (conv_params.C + conv_params.O) >= 256;
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if (conv_params.wS[0] == 3 && conv_params.wS[1] == 3 &&
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conv_params.C % 32 == 0 && conv_params.O % 32 == 0 && is_stride_one &&
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is_kdil_one && is_idil_one) {
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if (img_large && channels_large) {
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return winograd_conv_2D_gpu(s, d, in, wt, out, conv_params, copies);
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}
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if (conv_params.N <= 1) {
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return winograd_conv_2D_fused_gpu(s, d, in, wt, out, conv_params, copies);
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}
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if (!flip && is_stride_one && is_kdil_one && is_idil_one &&
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conv_params.wS[0] == 3 && conv_params.wS[1] == 3 &&
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conv_params.C % 32 == 0 && conv_params.O % 32 == 0 && inp_large &&
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channels_large) {
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return winograd_conv_2D_gpu(s, d, in, wt, out, conv_params, copies);
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}
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// Direct to implicit gemm conv
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@ -858,40 +876,8 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
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wt = arr_copy;
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}
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// Check for 1x1 conv
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auto is_one = [](int x) { return x == 1; };
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auto is_zero = [](int x) { return x == 0; };
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if (groups_ == 1 && (wt.shape(0) * wt.shape(-1) == wt.size()) &&
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std::all_of(wt.shape().begin() + 1, wt.shape().end() - 1, is_one) &&
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std::all_of(kernel_strides_.begin(), kernel_strides_.end(), is_one) &&
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std::all_of(input_dilation_.begin(), input_dilation_.end(), is_one) &&
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std::all_of(kernel_dilation_.begin(), kernel_dilation_.end(), is_one) &&
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std::all_of(padding_.begin(), padding_.end(), is_zero)) {
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std::vector<array> empty_copies;
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steel_matmul_regular(
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s,
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d,
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/*a = */ in,
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/*b = */ wt,
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/*c = */ out,
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/*M = */ in.size() / in.shape(-1),
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/*N = */ wt.shape(0),
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/*K = */ in.shape(-1),
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/*batch_size_out = */ 1,
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/*lda = */ in.shape(-1),
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/*ldb = */ wt.shape(-1),
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/*ldd = */ wt.shape(0),
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/*transpose_a = */ false,
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/*transpose_b = */ true,
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/*batch_shape = */ {1},
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/*batch_strides = */ {1},
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/*A_batch_stride = */ 0,
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/*B_batch_stride = */ 0,
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/*matrix_stride_out = */ 0,
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/*copies = */ empty_copies);
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}
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// 3D conv
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else if (out.ndim() == 5) {
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if (out.ndim() == 5) {
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conv_3D_gpu(
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s,
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d,
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@ -326,13 +326,7 @@ constant constexpr const float WinogradTransforms<6, 3, 8>::wt_transform[8][8];
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constant constexpr const float WinogradTransforms<6, 3, 8>::in_transform[8][8];
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constant constexpr const float WinogradTransforms<6, 3, 8>::out_transform[8][8];
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template <
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typename T,
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int BC = 32,
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int BO = 4,
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bool do_flip = false,
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int M = 6,
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int R = 3>
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template <typename T, int BC = 32, int BO = 4, int M = 6, int R = 3>
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[[kernel, max_total_threads_per_threadgroup(BO * 32)]] void
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winograd_conv_2d_weight_transform(
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const device T* wt_in [[buffer(0)]],
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@ -379,12 +373,7 @@ winograd_conv_2d_weight_transform(
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for (int kh = 0; kh < R; ++kh) {
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for (int kw = 0; kw < R; ++kw) {
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for (int kc = simd_lane_id; kc < BC; kc += 32) {
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if (do_flip) {
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Ws[simd_group_id][R - 1 - kh][R - 1 - kw][kc] =
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wt_in[kh * R * C + kw * C + kc];
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} else {
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Ws[simd_group_id][kh][kw][kc] = wt_in[kh * R * C + kw * C + kc];
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}
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Ws[simd_group_id][kh][kw][kc] = wt_in[kh * R * C + kw * C + kc];
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}
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}
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}
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@ -409,10 +398,10 @@ winograd_conv_2d_weight_transform(
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}
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}
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#define instantiate_winograd_conv_2d_weight_tr_base_2(name, itype, bc, f) \
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template [[host_name("winograd_conv_2d_weight_transform_" #name "_bc" #bc \
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"_flip" #f)]] [[kernel]] void \
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winograd_conv_2d_weight_transform<itype, bc, 4, f>( \
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#define instantiate_winograd_conv_2d_weight_transform_base(name, itype, bc) \
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template [[host_name("winograd_conv_2d_weight_transform_" #name \
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"_bc" #bc)]] [[kernel]] void \
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winograd_conv_2d_weight_transform<itype, bc>( \
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const device itype* wt_in [[buffer(0)]], \
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device itype* wt_out [[buffer(1)]], \
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const constant int& C [[buffer(2)]], \
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@ -421,10 +410,6 @@ winograd_conv_2d_weight_transform(
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uint simd_group_id [[simdgroup_index_in_threadgroup]], \
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uint simd_lane_id [[thread_index_in_simdgroup]]);
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#define instantiate_winograd_conv_2d_weight_transform_base(name, itype, bc) \
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instantiate_winograd_conv_2d_weight_tr_base_2(name, itype, bc, 0) \
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instantiate_winograd_conv_2d_weight_tr_base_2(name, itype, bc, 1)
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template <typename T, int BC, int WM, int WN, int M = 6, int R = 3>
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[[kernel, max_total_threads_per_threadgroup(WM* WN * 32)]] void
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winograd_conv_2d_input_transform(
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@ -460,17 +445,10 @@ winograd_conv_2d_input_transform(
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// Resolve input tile
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constexpr int TH = (A / WM);
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constexpr int TW = (A / WN);
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const int kh = TH * (simd_group_id / WN);
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const int kw = TW * (simd_group_id % WN);
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const int bh = M * tid.y + kh - params.pad[1];
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const int bw = M * tid.x + kw - params.pad[0];
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const bool is_edge_w_lo = bw < 0;
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const bool is_edge_h_lo = bh < 0;
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const bool is_edge_w_hi = bw + (TW - 1) >= params.iS[0];
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const bool is_edge_h_hi = bh + (TH - 1) >= params.iS[1];
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const bool is_edge =
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is_edge_w_lo || is_edge_h_lo || is_edge_w_hi || is_edge_h_hi;
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int kh = TH * (simd_group_id / WN);
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int kw = TW * (simd_group_id % WN);
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int bh = M * tid.y + kh;
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int bw = M * tid.x + kw;
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// Move to the correct input tile
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inp_in += tid.z * params.in_strides[0] + bh * params.in_strides[1] +
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@ -506,21 +484,8 @@ winograd_conv_2d_input_transform(
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for (int h = 0; h < TH; h++) {
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for (int w = 0; w < TW; w++) {
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const device T* in_ptr = inp_in + jump_in[h][w];
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if (is_edge) {
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if (((bh + h) < 0 || (bh + h) >= params.iS[1]) ||
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((bw + w) < 0 || (bw + w) >= params.iS[0])) {
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for (int c = simd_lane_id; c < BC; c += 32) {
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Is[kh + h][kw + w][c] = T(0);
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}
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} else {
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for (int c = simd_lane_id; c < BC; c += 32) {
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Is[kh + h][kw + w][c] = in_ptr[c];
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}
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}
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} else {
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for (int c = simd_lane_id; c < BC; c += 32) {
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Is[kh + h][kw + w][c] = in_ptr[c];
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}
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for (int c = simd_lane_id; c < BC; c += 32) {
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Is[kh + h][kw + w][c] = in_ptr[c];
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}
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}
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}
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@ -687,371 +652,3 @@ winograd_conv_2d_output_transform(
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instantiate_winograd_conv_2d(float32, float);
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instantiate_winograd_conv_2d(bfloat16, bfloat16_t);
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instantiate_winograd_conv_2d(float16, half); // clang-format on
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#include "mlx/backend/metal/kernels/steel/attn/mma.h"
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template <
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typename T,
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bool do_flip = false,
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int WM = 4,
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int WN = 1,
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typename AccumType = float>
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[[kernel]] void winograd_fused(
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const device T* input [[buffer(0)]],
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const device T* weight [[buffer(1)]],
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device T* output [[buffer(2)]],
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const constant MLXConvParams<2>& params [[buffer(3)]],
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uint3 tid [[threadgroup_position_in_grid]],
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uint3 lid [[thread_position_in_threadgroup]],
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uint3 tgp_per_grid [[threadgroups_per_grid]],
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ushort simd_group_id [[simdgroup_index_in_threadgroup]],
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ushort simd_lane_id [[thread_index_in_simdgroup]]) {
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using namespace mlx::steel;
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(void)tgp_per_grid;
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// Winograd F(n x n, r x r)
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// n x n output window
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constexpr short FN = 2;
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// r x r filter size
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constexpr short FR = 3;
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// a x a input window, a = n + r - 1
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constexpr short FA = 4;
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constexpr short kFragSize = 8; // MMA frag size
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constexpr short BT = 8; // Tile block size
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constexpr short BO = 8; // Output channel block size
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constexpr short BC = 8; // Input channel block size
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// clang-format off
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static_assert(BT % (1 * kFragSize) == 0 &&
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BO % (1 * kFragSize) == 0 &&
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BC % kFragSize == 0,
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"Matmuls sizes must be compatible with fragments");
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// clang-format on
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// Prepare for matmul
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// Warp tile sizes for matmul
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constexpr short TM = (FA * FA * BT) / (WM * kFragSize);
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constexpr short TN = (BO) / (WN * kFragSize);
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constexpr short TK = (BC) / (kFragSize);
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// Warp primitives
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using MMAFrag_acc_t = BaseMMAFrag<AccumType, kFragSize, kFragSize>;
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|
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// Warp tiles sizes for matmul
|
||||
MMATile<AccumType, 1, TK, MMAFrag_acc_t> Itile;
|
||||
MMATile<AccumType, TK, TN, MMAFrag_acc_t> Wtile;
|
||||
MMATile<AccumType, 1, TN, MMAFrag_acc_t> Otile[TM];
|
||||
|
||||
for (int im = 0; im < 4; im++) {
|
||||
Otile[im].clear();
|
||||
}
|
||||
|
||||
// Threadgroup memory for Weights and Inputs
|
||||
constexpr short BS = BT > BO ? BT : BO;
|
||||
threadgroup T Wt[FA * FA * BC * BO];
|
||||
threadgroup T It[FA * FA * BS * BS];
|
||||
|
||||
// Get thread position in tile
|
||||
short2 simd_coord = MMAFrag_acc_t::get_coord(simd_lane_id);
|
||||
const short sm = simd_coord.y;
|
||||
const short sn = simd_coord.x;
|
||||
|
||||
static_assert(FA * FA * BT == 32 * WM * WN, "Each thread loads one pixel.");
|
||||
const int thr_idx = simd_group_id * 32 + simd_lane_id;
|
||||
const int thr_t = thr_idx / (FA * FA);
|
||||
const int thr_hw = thr_idx % (FA * FA);
|
||||
const int thr_h = thr_hw / FA;
|
||||
const int thr_w = thr_hw % FA;
|
||||
|
||||
// Get batch, tile, and output idx for warp
|
||||
const int b_idx = tid.z;
|
||||
const int t_idx = BT * tid.y + thr_t;
|
||||
const int o_idx = BO * tid.x + thr_t;
|
||||
|
||||
// Divide tile into h, w tile
|
||||
uniform<int> oWu = make_uniform(params.oS[1]);
|
||||
uniform<int> tWu = (oWu + make_uniform(FN - 1)) / make_uniform(FN);
|
||||
|
||||
const int oH_idx = FN * (t_idx / tWu);
|
||||
const int oW_idx = FN * (t_idx % tWu);
|
||||
const int iH_idx = oH_idx + thr_h - params.pad[0];
|
||||
const int iW_idx = oW_idx + thr_w - params.pad[1];
|
||||
|
||||
// Move to correct location
|
||||
|
||||
// clang-format off
|
||||
input += b_idx * params.in_strides[0] + // N
|
||||
iH_idx * params.in_strides[1] + // H
|
||||
iW_idx * params.in_strides[2]; // W
|
||||
|
||||
weight += o_idx * params.wt_strides[0] + // O
|
||||
thr_h * params.wt_strides[1] + // H
|
||||
thr_w * params.wt_strides[2]; // W
|
||||
// clang-format on
|
||||
|
||||
// Do edge check prep for input
|
||||
const bool is_edge_w_lo = iH_idx < 0;
|
||||
const bool is_edge_h_lo = iW_idx < 0;
|
||||
const bool is_edge_w_hi = iH_idx >= params.iS[0];
|
||||
const bool is_edge_h_hi = iW_idx >= params.iS[1];
|
||||
const bool is_edge =
|
||||
is_edge_w_lo || is_edge_h_lo || is_edge_w_hi || is_edge_h_hi;
|
||||
|
||||
// Iterate over C
|
||||
for (int c = 0; c < params.C; c += BC) {
|
||||
#define tmp_load_wt_idx(o, h, w, c) h* FA* BC* BO + w* BC* BO + c* BO + o
|
||||
#define tmp_load_in_idx(t, h, w, c) h* FA* BS* BC + w* BS* BC + t* BC + c
|
||||
|
||||
#define tmp_trns_wt_idx(o, h, w, c) h* FA* BC* BO + w* BC* BO + c* BO + o
|
||||
#define tmp_trns_in_idx(t, h, w, c) h* FA* BS* BC + w* BS* BC + t* BC + c
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// Load weight
|
||||
if (thr_h < FR && thr_w < FR && thr_t < BO) {
|
||||
for (int ic = 0; ic < BC; ic++) {
|
||||
if (do_flip) {
|
||||
Wt[tmp_load_wt_idx(thr_t, FR - 1 - thr_h, FR - 1 - thr_w, ic)] =
|
||||
weight[c + ic];
|
||||
} else {
|
||||
Wt[tmp_load_wt_idx(thr_t, thr_h, thr_w, ic)] = weight[c + ic];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Load input
|
||||
if (is_edge) {
|
||||
for (int ic = 0; ic < BC; ic++) {
|
||||
It[tmp_load_in_idx(thr_t, thr_h, thr_w, ic)] = T(0);
|
||||
}
|
||||
} else {
|
||||
for (int ic = 0; ic < BC; ic++) {
|
||||
It[tmp_load_in_idx(thr_t, thr_h, thr_w, ic)] = input[c + ic];
|
||||
}
|
||||
}
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// Transform weight
|
||||
if (lid.z == 0) {
|
||||
const short ic = lid.y;
|
||||
const short io = lid.x;
|
||||
|
||||
T tmp_0[4][4];
|
||||
T tmp_1[4][4];
|
||||
|
||||
for (int ii = 0; ii < 3; ++ii) {
|
||||
for (int jj = 0; jj < 3; ++jj) {
|
||||
tmp_0[ii][jj] = Wt[tmp_load_wt_idx(io, ii, jj, ic)];
|
||||
}
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////
|
||||
|
||||
tmp_1[0][0] = tmp_0[0][0];
|
||||
tmp_1[0][1] = tmp_0[0][1];
|
||||
tmp_1[0][2] = tmp_0[0][2];
|
||||
|
||||
tmp_1[1][0] = T(0.5) * (tmp_0[0][0] + tmp_0[1][0] + tmp_0[2][0]);
|
||||
tmp_1[1][1] = T(0.5) * (tmp_0[0][1] + tmp_0[1][1] + tmp_0[2][1]);
|
||||
tmp_1[1][2] = T(0.5) * (tmp_0[0][2] + tmp_0[1][2] + tmp_0[2][2]);
|
||||
|
||||
tmp_1[2][0] = tmp_1[1][0] - tmp_0[1][0];
|
||||
tmp_1[2][1] = tmp_1[1][1] - tmp_0[1][1];
|
||||
tmp_1[2][2] = tmp_1[1][2] - tmp_0[1][2];
|
||||
|
||||
tmp_1[3][0] = tmp_0[2][0];
|
||||
tmp_1[3][1] = tmp_0[2][1];
|
||||
tmp_1[3][2] = tmp_0[2][2];
|
||||
|
||||
//////////////////////////////////////////////
|
||||
tmp_0[0][0] = tmp_1[0][0];
|
||||
tmp_0[1][0] = tmp_1[1][0];
|
||||
tmp_0[2][0] = tmp_1[2][0];
|
||||
tmp_0[3][0] = tmp_1[3][0];
|
||||
|
||||
tmp_0[0][1] = T(0.5) * (tmp_1[0][0] + tmp_1[0][1] + tmp_1[0][2]);
|
||||
tmp_0[1][1] = T(0.5) * (tmp_1[1][0] + tmp_1[1][1] + tmp_1[1][2]);
|
||||
tmp_0[2][1] = T(0.5) * (tmp_1[2][0] + tmp_1[2][1] + tmp_1[2][2]);
|
||||
tmp_0[3][1] = T(0.5) * (tmp_1[3][0] + tmp_1[3][1] + tmp_1[3][2]);
|
||||
|
||||
tmp_0[0][2] = tmp_0[0][1] - tmp_1[0][1];
|
||||
tmp_0[1][2] = tmp_0[1][1] - tmp_1[1][1];
|
||||
tmp_0[2][2] = tmp_0[2][1] - tmp_1[2][1];
|
||||
tmp_0[3][2] = tmp_0[3][1] - tmp_1[3][1];
|
||||
|
||||
tmp_0[0][3] = tmp_1[0][2];
|
||||
tmp_0[1][3] = tmp_1[1][2];
|
||||
tmp_0[2][3] = tmp_1[2][2];
|
||||
tmp_0[3][3] = tmp_1[3][2];
|
||||
|
||||
for (int ii = 0; ii < 4; ++ii) {
|
||||
for (int jj = 0; jj < 4; ++jj) {
|
||||
Wt[tmp_trns_wt_idx(io, ii, jj, ic)] = tmp_0[ii][jj];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Transform input
|
||||
else {
|
||||
const short it = lid.y;
|
||||
const short ic = lid.x;
|
||||
|
||||
T tmp_0[4][4];
|
||||
T tmp_1[4][4];
|
||||
|
||||
for (int ii = 0; ii < 4; ++ii) {
|
||||
for (int jj = 0; jj < 4; ++jj) {
|
||||
tmp_0[ii][jj] = It[tmp_load_in_idx(it, ii, jj, ic)];
|
||||
}
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////
|
||||
|
||||
tmp_1[0][0] = tmp_0[0][0] - tmp_0[2][0];
|
||||
tmp_1[0][1] = tmp_0[0][1] - tmp_0[2][1];
|
||||
tmp_1[0][2] = tmp_0[0][2] - tmp_0[2][2];
|
||||
tmp_1[0][3] = tmp_0[0][3] - tmp_0[2][3];
|
||||
|
||||
tmp_1[1][0] = tmp_0[1][0] + tmp_0[2][0];
|
||||
tmp_1[1][1] = tmp_0[1][1] + tmp_0[2][1];
|
||||
tmp_1[1][2] = tmp_0[1][2] + tmp_0[2][2];
|
||||
tmp_1[1][3] = tmp_0[1][3] + tmp_0[2][3];
|
||||
|
||||
tmp_1[2][0] = tmp_0[2][0] - tmp_0[1][0];
|
||||
tmp_1[2][1] = tmp_0[2][1] - tmp_0[1][1];
|
||||
tmp_1[2][2] = tmp_0[2][2] - tmp_0[1][2];
|
||||
tmp_1[2][3] = tmp_0[2][3] - tmp_0[1][3];
|
||||
|
||||
tmp_1[3][0] = tmp_0[1][0] - tmp_0[3][0];
|
||||
tmp_1[3][1] = tmp_0[1][1] - tmp_0[3][1];
|
||||
tmp_1[3][2] = tmp_0[1][2] - tmp_0[3][2];
|
||||
tmp_1[3][3] = tmp_0[1][3] - tmp_0[3][3];
|
||||
|
||||
//////////////////////////////////////////////
|
||||
tmp_0[0][0] = tmp_1[0][0] - tmp_1[0][2];
|
||||
tmp_0[1][0] = tmp_1[1][0] - tmp_1[1][2];
|
||||
tmp_0[2][0] = tmp_1[2][0] - tmp_1[2][2];
|
||||
tmp_0[3][0] = tmp_1[3][0] - tmp_1[3][2];
|
||||
|
||||
tmp_0[0][1] = tmp_1[0][1] + tmp_1[0][2];
|
||||
tmp_0[1][1] = tmp_1[1][1] + tmp_1[1][2];
|
||||
tmp_0[2][1] = tmp_1[2][1] + tmp_1[2][2];
|
||||
tmp_0[3][1] = tmp_1[3][1] + tmp_1[3][2];
|
||||
|
||||
tmp_0[0][2] = tmp_1[0][2] - tmp_1[0][1];
|
||||
tmp_0[1][2] = tmp_1[1][2] - tmp_1[1][1];
|
||||
tmp_0[2][2] = tmp_1[2][2] - tmp_1[2][1];
|
||||
tmp_0[3][2] = tmp_1[3][2] - tmp_1[3][1];
|
||||
|
||||
tmp_0[0][3] = tmp_1[0][1] - tmp_1[0][3];
|
||||
tmp_0[1][3] = tmp_1[1][1] - tmp_1[1][3];
|
||||
tmp_0[2][3] = tmp_1[2][1] - tmp_1[2][3];
|
||||
tmp_0[3][3] = tmp_1[3][1] - tmp_1[3][3];
|
||||
|
||||
for (int ii = 0; ii < 4; ++ii) {
|
||||
for (int jj = 0; jj < 4; ++jj) {
|
||||
It[tmp_trns_in_idx(it, ii, jj, ic)] = tmp_0[ii][jj];
|
||||
}
|
||||
}
|
||||
}
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// Do matmul
|
||||
for (int im = 0; im < 4; im++) {
|
||||
simdgroup_barrier(mem_flags::mem_none);
|
||||
Itile.template load<T, 1, 1, BS, 1>(
|
||||
&It[simd_group_id * FA * BS * BS + im * BS * BS + sm * BS + sn]);
|
||||
simdgroup_barrier(mem_flags::mem_none);
|
||||
Wtile.template load<T, 1, 1, BO, 1>(
|
||||
&Wt[simd_group_id * FA * BC * BO + im * BC * BO + sm * BO + sn]);
|
||||
simdgroup_barrier(mem_flags::mem_none);
|
||||
tile_matmad(Otile[im], Itile, Wtile, Otile[im]);
|
||||
}
|
||||
}
|
||||
|
||||
// Transform and write output
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
for (int im = 0; im < 4; im++) {
|
||||
Otile[im].template store<T, 1, 1, BS, 1>(
|
||||
&It[simd_group_id * FA * BS * BS + im * BS * BS + sm * BS + sn]);
|
||||
}
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (lid.z == 0) {
|
||||
const short it = lid.y;
|
||||
const short io = lid.x;
|
||||
|
||||
T tmp_0[4][4];
|
||||
T tmp_1[2][4];
|
||||
T tmp_2[2][2];
|
||||
|
||||
for (int ii = 0; ii < 4; ++ii) {
|
||||
for (int jj = 0; jj < 4; ++jj) {
|
||||
tmp_0[ii][jj] = It[tmp_trns_in_idx(it, ii, jj, io)];
|
||||
}
|
||||
}
|
||||
|
||||
tmp_1[0][0] = tmp_0[0][0] + tmp_0[1][0] + tmp_0[2][0];
|
||||
tmp_1[0][1] = tmp_0[0][1] + tmp_0[1][1] + tmp_0[2][1];
|
||||
tmp_1[0][2] = tmp_0[0][2] + tmp_0[1][2] + tmp_0[2][2];
|
||||
tmp_1[0][3] = tmp_0[0][3] + tmp_0[1][3] + tmp_0[2][3];
|
||||
|
||||
tmp_1[1][0] = tmp_0[1][0] - tmp_0[2][0] - tmp_0[3][0];
|
||||
tmp_1[1][1] = tmp_0[1][1] - tmp_0[2][1] - tmp_0[3][1];
|
||||
tmp_1[1][2] = tmp_0[1][2] - tmp_0[2][2] - tmp_0[3][2];
|
||||
tmp_1[1][3] = tmp_0[1][3] - tmp_0[2][3] - tmp_0[3][3];
|
||||
|
||||
tmp_2[0][0] = tmp_1[0][0] + tmp_1[0][1] + tmp_1[0][2];
|
||||
tmp_2[1][0] = tmp_1[1][0] + tmp_1[1][1] + tmp_1[1][2];
|
||||
|
||||
tmp_2[0][1] = tmp_1[0][1] - tmp_1[0][2] - tmp_1[0][3];
|
||||
tmp_2[1][1] = tmp_1[1][1] - tmp_1[1][2] - tmp_1[1][3];
|
||||
|
||||
const int oH_i = FN * ((BT * tid.y + it) / tWu);
|
||||
const int oW_i = FN * ((BT * tid.y + it) % tWu);
|
||||
|
||||
// clang-format off
|
||||
output += b_idx * params.out_strides[0] + // N
|
||||
oH_i * params.out_strides[1] + // H
|
||||
oW_i * params.out_strides[2] + // W
|
||||
BO * tid.x; // C
|
||||
|
||||
// clang-format on
|
||||
|
||||
output[0 * params.out_strides[1] + 0 * params.out_strides[2] + io] =
|
||||
tmp_2[0][0];
|
||||
output[0 * params.out_strides[1] + 1 * params.out_strides[2] + io] =
|
||||
tmp_2[0][1];
|
||||
output[1 * params.out_strides[1] + 0 * params.out_strides[2] + io] =
|
||||
tmp_2[1][0];
|
||||
output[1 * params.out_strides[1] + 1 * params.out_strides[2] + io] =
|
||||
tmp_2[1][1];
|
||||
}
|
||||
}
|
||||
|
||||
// clang-format off
|
||||
#define instantiate_winograd_conv_2d_fused(name, itype, f) \
|
||||
template [[host_name("winograd_conv_2d_fused_" #name "_flip" #f)]] \
|
||||
[[kernel]] void winograd_fused<itype, f>( \
|
||||
const device itype* input [[buffer(0)]], \
|
||||
const device itype* weight [[buffer(1)]], \
|
||||
device itype* output [[buffer(2)]], \
|
||||
const constant MLXConvParams<2>& params [[buffer(3)]], \
|
||||
uint3 tid [[threadgroup_position_in_grid]], \
|
||||
uint3 lid [[thread_position_in_threadgroup]], \
|
||||
uint3 tgp_per_grid [[threadgroups_per_grid]], \
|
||||
ushort simd_group_id [[simdgroup_index_in_threadgroup]], \
|
||||
ushort simd_lane_id [[thread_index_in_simdgroup]]);
|
||||
|
||||
#define instantiate_winograd_conv_2d_fused_2(name, itype) \
|
||||
instantiate_winograd_conv_2d_fused(name, itype, 0) \
|
||||
instantiate_winograd_conv_2d_fused(name, itype, 1)
|
||||
|
||||
instantiate_winograd_conv_2d_fused_2(float32, float);
|
||||
instantiate_winograd_conv_2d_fused_2(float16, float16_t);
|
||||
instantiate_winograd_conv_2d_fused_2(bfloat16, bfloat16_t);
|
||||
// clang-format on
|
||||
|
||||
@ -3882,7 +3882,7 @@ array conv_general(
|
||||
|
||||
return array(
|
||||
std::move(out_shape),
|
||||
out_type,
|
||||
in.dtype(),
|
||||
std::make_shared<Convolution>(
|
||||
to_stream(s),
|
||||
stride,
|
||||
|
||||
@ -341,7 +341,7 @@ class TestConv(mlx_tests.MLXTestCase):
|
||||
atol, rtol = 1e-1, 1e-3
|
||||
else:
|
||||
atol, rtol = 1e-5, 1e-6
|
||||
self.assertTrue(np.allclose(out_pt, out_mx, atol=atol, rtol=rtol))
|
||||
self.assertTrue(np.allclose(out_pt, out_mx, atol=atol))
|
||||
|
||||
for dtype in ("float32", "bfloat16"):
|
||||
for N, C, O in (
|
||||
@ -1042,6 +1042,14 @@ class TestConv(mlx_tests.MLXTestCase):
|
||||
self.assertTrue(mx.allclose(expected[0], grads[0]))
|
||||
self.assertTrue(mx.allclose(expected[1], grads[1]))
|
||||
|
||||
def test_repeated_conv(self):
|
||||
x = mx.random.normal((1, 3, 3, 320))
|
||||
w = mx.random.normal((320, 3, 3, 320))
|
||||
for i in range(8):
|
||||
y1 = mx.conv2d(x, w, (1, 1), (1, 1), (1, 1), 1)
|
||||
y2 = mx.conv2d(x, w, (1, 1), (1, 1), (1, 1), 1)
|
||||
self.assertTrue(mx.allclose(y1, y2))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user