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vulkan : kernels for depthwise 2D convolution (CONV_2D_DW) #1204
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,104 @@ | ||
| #version 450 | ||
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| #include "types.comp" | ||
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| layout (push_constant) uniform parameter | ||
| { | ||
| uint ne; | ||
| uint batches; | ||
| uint channels; | ||
| uint dst_w; | ||
| uint dst_h; | ||
| uint src_w; | ||
| uint src_h; | ||
| uint knl_w; | ||
| uint knl_h; | ||
| int stride_x; | ||
| int stride_y; | ||
| int pad_x; | ||
| int pad_y; | ||
| int dilation_x; | ||
| int dilation_y; | ||
| } p; | ||
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| layout (binding = 0) readonly buffer A {A_TYPE knl_data[];}; | ||
| layout (binding = 1) readonly buffer B {B_TYPE src_data[];}; | ||
| layout (binding = 2) writeonly buffer D {D_TYPE dst_data[];}; | ||
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| layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; | ||
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| FLOAT_TYPE conv_2d_dw_whcn(uint idx) { | ||
| uint i0 = idx / p.dst_w; | ||
| uint dst_x = idx - i0 * p.dst_w; | ||
| uint i1 = i0 / p.dst_h; | ||
| uint dst_y = i0 - i1 * p.dst_h; | ||
| uint n = i1 / p.channels; | ||
| uint c = i1 - n * p.channels; | ||
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| uint src_i = n * p.channels * p.src_h * p.src_w + c * p.src_h * p.src_w; | ||
| uint knl_i = c * p.knl_h * p.knl_w; | ||
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| FLOAT_TYPE sum = 0.0; | ||
| for (uint knl_y = 0; knl_y < p.knl_h; ++knl_y) { | ||
| uint src_y = dst_y * p.stride_y - p.pad_y + knl_y * p.dilation_y; | ||
| if (src_y < 0 || src_y >= p.src_h) { | ||
| continue; | ||
| } | ||
| for (uint knl_x = 0; knl_x < p.knl_w; ++knl_x) { | ||
| uint src_x = dst_x * p.stride_x - p.pad_x + knl_x * p.dilation_x; | ||
| if (src_x < 0 || src_x >= p.src_w) { | ||
| continue; | ||
| } | ||
| FLOAT_TYPE v = FLOAT_TYPE(src_data[src_i + src_y * p.src_w + src_x]); | ||
| FLOAT_TYPE k = FLOAT_TYPE(knl_data[knl_i + knl_y * p.knl_w + knl_x]); | ||
| sum = fma(v, k, sum); | ||
| } | ||
| } | ||
| return sum; | ||
| } | ||
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| FLOAT_TYPE conv_2d_dw_cwhn(uint idx) { | ||
| uint i0 = idx / p.channels; | ||
| uint c = idx - i0 * p.channels; | ||
| uint i1 = i0 / p.dst_w; | ||
| uint dst_x = i0 - i1 * p.dst_w; | ||
| uint n = i1 / p.dst_h; | ||
| uint dst_y = i1 - n * p.dst_h; | ||
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| uint src_i = n * p.channels * p.src_h * p.src_w; | ||
| uint src_row = p.src_w * p.channels; | ||
| uint knl_row = p.knl_w * p.channels; | ||
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| FLOAT_TYPE sum = 0.0; | ||
| for (uint knl_y = 0; knl_y < p.knl_h; ++knl_y) { | ||
| uint src_y = dst_y * p.stride_y + knl_y * p.dilation_y - p.pad_y; | ||
| if (src_y < 0 || src_y >= p.src_h) { | ||
| continue; | ||
| } | ||
| for (uint knl_x = 0; knl_x < p.knl_w; ++knl_x) { | ||
| uint src_x = dst_x * p.stride_x + knl_x * p.dilation_x - p.pad_x; | ||
| if (src_x < 0 || src_x >= p.src_w) { | ||
| continue; | ||
|
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I guess padding is always considered to be with a value of zero, never replicating the border? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Yes, there is no way to specify padding modes other than zero for convolutions so far. |
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| } | ||
| FLOAT_TYPE v = FLOAT_TYPE(src_data[src_i + src_y * src_row + src_x * p.channels + c]); | ||
| FLOAT_TYPE k = FLOAT_TYPE(knl_data[ knl_y * knl_row + knl_x * p.channels + c]); | ||
| sum = fma(v, k, sum); | ||
| } | ||
| } | ||
| return sum; | ||
| } | ||
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| void main() { | ||
| uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; | ||
| if (idx >= p.ne) { | ||
| return; | ||
| } | ||
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| FLOAT_TYPE result = | ||
| #ifdef WHCN | ||
| conv_2d_dw_whcn(idx); | ||
| #else | ||
| conv_2d_dw_cwhn(idx); | ||
| #endif | ||
| dst_data[idx] = D_TYPE(result); | ||
| } | ||
| Original file line number | Diff line number | Diff line change |
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@@ -2760,6 +2760,48 @@ struct test_im2col : public test_case { | |
| } | ||
| }; | ||
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| // GGML_OP_CONV_2D_DW | ||
| struct test_conv_2d_dw : public test_case { | ||
| const std::array<int64_t, 4> ne_input; | ||
| const std::array<int64_t, 4> ne_kernel; | ||
| const int stride; | ||
| const int padding; | ||
| const int dilation; | ||
| const bool cwhn; | ||
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| std::string vars() override { | ||
| return VARS_TO_STR6(ne_input, ne_kernel, stride, padding, dilation, cwhn); | ||
| } | ||
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| test_conv_2d_dw(std::array<int64_t, 4> ne_input = {64, 64, 16, 1}, | ||
| std::array<int64_t, 4> ne_kernel = {3, 3, 1, 16}, | ||
| int stride = 1, int padding = 0, int dilation = 1, bool cwhn = false) | ||
| : ne_input(ne_input), ne_kernel(ne_kernel), stride(stride), padding(padding), dilation(dilation), cwhn(cwhn) {} | ||
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| ggml_tensor * build_graph(ggml_context * ctx) override { | ||
| ggml_tensor * input = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_input.data()); | ||
| ggml_set_name(input, "input"); | ||
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| ggml_tensor * kernel = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_kernel.data()); | ||
| ggml_set_name(kernel, "kernel"); | ||
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| if (cwhn) { | ||
| // change memory layout to channel-most-contiguous (CWHN), | ||
| // then permute it back so NE matches the original input | ||
| input = ggml_cont(ctx, ggml_permute(ctx, input, 1, 2, 0, 3)); | ||
| input = ggml_permute(ctx, input, 2, 0, 1, 3); | ||
| kernel = ggml_cont(ctx, ggml_permute(ctx, kernel, 2, 3, 1, 0)); | ||
| kernel = ggml_permute(ctx, kernel, 3, 2, 0, 1); | ||
| } | ||
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| ggml_tensor * out = ggml_conv_2d_dw_direct( | ||
| ctx, kernel, input, | ||
| stride, stride, padding, padding, dilation, dilation); | ||
| ggml_set_name(out, "out"); | ||
| return out; | ||
| } | ||
| }; | ||
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| // GGML_OP_CONCAT | ||
| struct test_concat : public test_case { | ||
| const ggml_type type; | ||
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@@ -3970,6 +4012,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() { | |
| // test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {1024, 1024, 256, 1}, {3, 3, 256, 1}, 1, 1, 1, 1, 1, 1, true)); | ||
| // test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32, {1024, 1024, 256, 1}, {3, 3, 256, 1}, 1, 1, 1, 1, 1, 1, true)); | ||
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| test_cases.emplace_back(new test_conv_2d_dw({17, 34, 9, 1}, {3, 3, 1, 9}, 1, 0, 1, false)); | ||
| test_cases.emplace_back(new test_conv_2d_dw({17, 34, 9, 1}, {3, 3, 1, 9}, 1, 0, 1, true)); | ||
| test_cases.emplace_back(new test_conv_2d_dw({32, 8, 64, 1}, {3, 3, 1, 64}, 2, 1, 1, false)); | ||
| test_cases.emplace_back(new test_conv_2d_dw({32, 8, 64, 1}, {3, 3, 1, 64}, 2, 1, 1, true)); | ||
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Please add perf tests that correspond to the examples you gave in the PR. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Done |
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| test_cases.emplace_back(new test_conv_transpose_1d()); | ||
| test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {2,3,2,1}, 3, 0, 1)); | ||
| test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {2,3,2,1}, 2, 0, 1)); | ||
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Since src_x and src_y are unsigned, the
< 0conditions can be removed.There was a problem hiding this comment.
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Is it possible
src_yunderflows if pad_y is large enough?There was a problem hiding this comment.
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Yes, if there is padding the expression can become negative and wraps to a very large unsigned int, which will then be caught by the
>=check (for typical values). So in the end it does what's intended, and thesrc_y < 0check can be omitted.The alternative is to use signed int and keep the check (bit cleaner, more instructions). Using unsigned and the check as it is makes no sense, I'll fix that.
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Removed the check and added a comment to indicate wrapping is intentional.