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	Fix copying scalars by adding fill_gpu (#1402)
* fix copying scalars by adding fill_gpu * Another copy scalar changed to fill --------- Co-authored-by: Angelos Katharopoulos <a_katharopoulos@apple.com>
This commit is contained in:
		| @@ -552,7 +552,7 @@ void winograd_conv_2D_gpu( | ||||
|  | ||||
|   // Fill with zeros | ||||
|   array zero_arr = array(0, in.dtype()); | ||||
|   copy_gpu(zero_arr, in_padded, CopyType::Scalar, s); | ||||
|   fill_gpu(zero_arr, in_padded, s); | ||||
|   copies_w.push_back(zero_arr); | ||||
|  | ||||
|   // Pick input slice from padded | ||||
| @@ -571,7 +571,6 @@ void winograd_conv_2D_gpu( | ||||
|  | ||||
|   copies_w.push_back(in_padded_slice); | ||||
|   copies_w.push_back(in_padded); | ||||
|   copies_w.push_back(zero_arr); | ||||
|  | ||||
|   MLXConvParams<2> conv_params_updated{ | ||||
|       /* const int  N = */ in_padded.shape(0), | ||||
|   | ||||
| @@ -174,4 +174,31 @@ void copy_gpu_inplace( | ||||
|       in, out, in.shape(), istride, ostrides, ioffset, 0, ctype, s); | ||||
| } | ||||
|  | ||||
| void fill_gpu(const array& val, array& out, const Stream& s) { | ||||
|   if (out.size() == 0) { | ||||
|     return; | ||||
|   } | ||||
|   out.set_data(allocator::malloc_or_wait(out.nbytes())); | ||||
|   bool use_2d = out.data_size() > UINT32_MAX; | ||||
|   auto& d = metal::device(s.device); | ||||
|   std::string kernel_name = std::string(use_2d ? "s2" : "s") + "_copy" + | ||||
|       type_to_name(val) + type_to_name(out); | ||||
|   auto kernel = get_copy_kernel(d, kernel_name, val, out); | ||||
|   auto& compute_encoder = d.get_command_encoder(s.index); | ||||
|   compute_encoder->setComputePipelineState(kernel); | ||||
|  | ||||
|   compute_encoder.set_input_array(val, 0); | ||||
|   compute_encoder.set_output_array(out, 1); | ||||
|  | ||||
|   size_t nthreads = out.data_size(); | ||||
|   MTL::Size grid_dims = use_2d ? get_2d_grid_dims(out.shape(), out.strides()) | ||||
|                                : MTL::Size(nthreads, 1, 1); | ||||
|   NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup(); | ||||
|   if (thread_group_size > nthreads) { | ||||
|     thread_group_size = nthreads; | ||||
|   } | ||||
|   MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1); | ||||
|   compute_encoder.dispatchThreads(grid_dims, group_dims); | ||||
| } | ||||
|  | ||||
| } // namespace mlx::core | ||||
|   | ||||
| @@ -37,4 +37,7 @@ void copy_gpu_inplace( | ||||
|     CopyType ctype, | ||||
|     const Stream& s); | ||||
|  | ||||
| // Fill the output with the scalar val | ||||
| void fill_gpu(const array& val, array& out, const Stream& s); | ||||
|  | ||||
| } // namespace mlx::core | ||||
|   | ||||
| @@ -17,9 +17,8 @@ void CustomKernel::eval_gpu( | ||||
|   for (auto& out : outputs) { | ||||
|     out.set_data(allocator::malloc_or_wait(out.nbytes())); | ||||
|     if (init_value_) { | ||||
|       array init = array(init_value_.value(), out.dtype()); | ||||
|       copy_gpu(init, out, CopyType::Scalar, s); | ||||
|       copies.push_back(init); | ||||
|       copies.emplace_back(init_value_.value(), out.dtype()); | ||||
|       fill_gpu(copies.back(), out, s); | ||||
|     } | ||||
|   } | ||||
|  | ||||
|   | ||||
| @@ -526,7 +526,7 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) { | ||||
|   // Return 0s if either input is empty | ||||
|   if (a_pre.size() == 0 || b_pre.size() == 0) { | ||||
|     array zero = array(0, a_pre.dtype()); | ||||
|     copy_gpu(zero, out, CopyType::Scalar, s); | ||||
|     fill_gpu(zero, out, s); | ||||
|     auto command_buffer = d.get_command_buffer(s.index); | ||||
|     command_buffer->addCompletedHandler([zero](MTL::CommandBuffer*) {}); | ||||
|     return; | ||||
| @@ -1156,7 +1156,7 @@ void BlockMaskedMM::eval_gpu(const std::vector<array>& inputs, array& out) { | ||||
|   // Return 0s if either input is empty | ||||
|   if (a_pre.size() == 0 || b_pre.size() == 0) { | ||||
|     array zero = array(0, a_pre.dtype()); | ||||
|     copy_gpu(zero, out, CopyType::Scalar, s); | ||||
|     fill_gpu(zero, out, s); | ||||
|     auto command_buffer = d.get_command_buffer(s.index); | ||||
|     command_buffer->addCompletedHandler([zero](MTL::CommandBuffer*) {}); | ||||
|     return; | ||||
| @@ -1565,7 +1565,7 @@ void GatherMM::eval_gpu(const std::vector<array>& inputs, array& out) { | ||||
|   // Return 0s if either input is empty | ||||
|   if (a_pre.size() == 0 || b_pre.size() == 0) { | ||||
|     array zero = array(0, a_pre.dtype()); | ||||
|     copy_gpu(zero, out, CopyType::Scalar, s); | ||||
|     fill_gpu(zero, out, s); | ||||
|     auto command_buffer = d.get_command_buffer(s.index); | ||||
|     command_buffer->addCompletedHandler([zero](MTL::CommandBuffer*) {}); | ||||
|     return; | ||||
|   | ||||
| @@ -85,7 +85,7 @@ void pad_gpu( | ||||
|     std::vector<int> low_pad_size, | ||||
|     const Stream& s) { | ||||
|   // Fill output with val | ||||
|   copy_gpu(val, out, CopyType::Scalar, s); | ||||
|   fill_gpu(val, out, s); | ||||
|  | ||||
|   // Find offset for start of input values | ||||
|   size_t data_offset = 0; | ||||
|   | ||||
| @@ -2463,6 +2463,28 @@ TEST_CASE("test pad") { | ||||
|   CHECK_EQ(pad(x, 1).shape(), std::vector<int>{3, 4, 5}); | ||||
|   CHECK_EQ(pad(x, {0, 1}).shape(), std::vector<int>{2, 3, 4}); | ||||
|   CHECK_EQ(pad(x, {{1, 1}, {1, 2}, {3, 1}}).shape(), std::vector<int>{3, 5, 7}); | ||||
|  | ||||
|   x = array({1.0f, 2.0f, 3.0f, 4.0f}, {2, 2}); | ||||
|   auto padded_x = pad(x, 1); | ||||
|   auto expected = array( | ||||
|       {0.0f, | ||||
|        0.0f, | ||||
|        0.0f, | ||||
|        0.0f, | ||||
|        0.0f, | ||||
|        1.0f, | ||||
|        2.0f, | ||||
|        0.0f, | ||||
|        0.0f, | ||||
|        3.0f, | ||||
|        4.0f, | ||||
|        0.0f, | ||||
|        0.0f, | ||||
|        0.0f, | ||||
|        0.0f, | ||||
|        0.0f}, | ||||
|       {4, 4}); | ||||
|   CHECK(array_equal(padded_x, expected).item<bool>()); | ||||
| } | ||||
|  | ||||
| TEST_CASE("test power") { | ||||
|   | ||||
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