diff --git a/.github/workflows/ttnn-run-sweeps.yaml b/.github/workflows/ttnn-run-sweeps.yaml index efddf597300..bbc750c0b39 100644 --- a/.github/workflows/ttnn-run-sweeps.yaml +++ b/.github/workflows/ttnn-run-sweeps.yaml @@ -401,6 +401,7 @@ on: - conv2d.full.conv2d_sharding - conv2d.full.conv2d_sliding_window - conv2d.short.conv2d_short_sweep + - conv2d.short.conv2d_ttforge_sweep - pooling.global_avg_pool2d - pooling.max_pool2d - max_pool2d.short.max_pool2d_short_sweep diff --git a/models/experimental/functional_unet/tests/test_unet_perf.py b/models/experimental/functional_unet/tests/test_unet_perf.py index b2d022ff08a..dcde736b46c 100644 --- a/models/experimental/functional_unet/tests/test_unet_perf.py +++ b/models/experimental/functional_unet/tests/test_unet_perf.py @@ -34,7 +34,7 @@ @pytest.mark.models_device_performance_bare_metal @pytest.mark.parametrize( "batch, groups, expected_device_perf_fps", - ((1, 2, 1053.0),), + ((1, 2, 1040.0),), ) def test_unet_perf_device(batch: int, groups: int, expected_device_perf_fps: float): command = f"pytest models/experimental/functional_unet/tests/test_unet_model.py::test_unet_model[device_params0-{groups}-{batch}]" diff --git a/tests/sweep_framework/sweep_utils/conv2d_common.py b/tests/sweep_framework/sweep_utils/conv2d_common.py index a7354f8363a..c6f2abba90d 100644 --- a/tests/sweep_framework/sweep_utils/conv2d_common.py +++ b/tests/sweep_framework/sweep_utils/conv2d_common.py @@ -178,51 +178,109 @@ def run_conv2d_short_sweep( input_specs, device, ) -> list: - [ - batch_size, - output_channels, - input_channels, - input_height, - input_width, - kernel_height, - kernel_width, - stride_h, - stride_w, - pad_h, - pad_w, - groups, - has_bias, - dilation, - ] = input_specs + # for tt-forge suite, extra arguments are tensor configs + is_forge_suite = False + if len(input_specs) > 15: + [ + batch_size, + output_channels, + input_channels, + input_height, + input_width, + kernel_height, + kernel_width, + stride_h, + stride_w, + pad_h, + pad_w, + groups, + dilation_h, + dilation_w, + has_bias, + [input_layout, input_buffer_type, input_dtype], + [weights_layout, weights_buffer_type, weights_dtype], + [output_layout, output_buffer_type, output_dtype], + ] = input_specs + is_forge_suite = True + else: + [ + batch_size, + output_channels, + input_channels, + input_height, + input_width, + kernel_height, + kernel_width, + stride_h, + stride_w, + pad_h, + pad_w, + groups, + dilation_h, + dilation_w, + has_bias, + ] = input_specs print(input_specs) + if is_forge_suite: + torch_input_dtype = torch.bfloat16 if input_dtype == ttnn.DataType(ttnn.bfloat16) else torch.float32 + torch_weight_dtype = torch.bfloat16 if weights_dtype == ttnn.DataType(ttnn.bfloat16) else torch.float32 + conv_input_shape = [batch_size, input_channels, input_height, input_width] conv_weight_shape = [output_channels, input_channels // groups, kernel_height, kernel_width] conv_bias_shape = [1, 1, 1, output_channels] - torch_input_tensor_nchw = torch.randn(conv_input_shape, dtype=torch.bfloat16).float() + torch_input_tensor_nchw = torch.randn( + conv_input_shape, dtype=torch_input_dtype if is_forge_suite else torch.bfloat16 + ).float() torch_input_tensor = torch.permute(torch_input_tensor_nchw, (0, 2, 3, 1)) - torch_weight_tensor = torch.randn(conv_weight_shape, dtype=torch.bfloat16).float() + torch_weight_tensor = torch.randn( + conv_weight_shape, dtype=torch_weight_dtype if is_forge_suite else torch.bfloat16 + ).float() torch_bias_tensor = None if has_bias: - torch_bias_tensor = torch.randn(conv_bias_shape, dtype=torch.bfloat16).float() if has_bias else None + torch_bias_tensor = ( + torch.randn(conv_bias_shape, dtype=torch_weight_dtype if is_forge_suite else torch.bfloat16).float() + if has_bias + else None + ) torch_out_golden_tensor = torch.nn.functional.conv2d( torch_input_tensor_nchw, torch_weight_tensor, bias=torch_bias_tensor.reshape(-1) if has_bias else None, stride=(stride_h, stride_w), padding=(pad_h, pad_w), - dilation=(dilation, dilation), + dilation=(dilation_h, dilation_w), groups=groups, ) - tt_weight_tensor = ttnn.from_torch(torch_weight_tensor, ttnn.bfloat16) tt_bias_tensor = None - if has_bias: - tt_bias_tensor = ttnn.from_torch(torch_bias_tensor, ttnn.bfloat16) + if is_forge_suite: + input_layout = ttnn.Layout(input_layout) + input_dtype = ttnn.DataType(input_dtype) + input_memory_config = ttnn.DRAM_MEMORY_CONFIG if input_buffer_type == "dram" else ttnn.L1_MEMORY_CONFIG + tt_input_tensor = ttnn.from_torch( + torch_input_tensor, dtype=input_dtype, layout=input_layout, device=device, memory_config=input_memory_config + ) + weights_dtype = ttnn.DataType(weights_dtype) + tt_weight_tensor = ttnn.from_torch(torch_weight_tensor, weights_dtype) + if has_bias: + tt_bias_tensor = ttnn.from_torch(torch_bias_tensor, weights_dtype) + output_layout = ttnn.Layout(output_layout) + output_dtype = ttnn.DataType(output_dtype) + conv_config = ttnn.Conv2dConfig( + dtype=output_dtype, + weights_dtype=weights_dtype, + output_layout=output_layout, + ) + else: + tt_weight_tensor = ttnn.from_torch(torch_weight_tensor, ttnn.bfloat16) + if has_bias: + tt_bias_tensor = ttnn.from_torch(torch_bias_tensor, ttnn.bfloat16) - tt_input_tensor = ttnn.from_torch(torch_input_tensor, ttnn.bfloat16) + tt_input_tensor = ttnn.from_torch(torch_input_tensor, ttnn.bfloat16) + conv_config = ttnn.Conv2dConfig() start_time = start_measuring_time() [tt_output_tensor_on_device, [out_height, out_width], [weights_device, bias_device]] = ttnn.conv2d( @@ -235,11 +293,12 @@ def run_conv2d_short_sweep( kernel_size=(kernel_height, kernel_width), stride=(stride_h, stride_w), padding=(pad_h, pad_w), - dilation=(dilation, dilation), + dilation=(dilation_h, dilation_w), batch_size=batch_size, input_height=input_height, input_width=input_width, groups=groups, + conv_config=conv_config, return_output_dim=True, return_weights_and_bias=True, ) @@ -255,6 +314,7 @@ def run_conv2d_short_sweep( torch_output_tensor = torch.permute(torch_output_tensor, (0, 3, 1, 2)) + print("End of test case") return [check_with_pcc(torch_output_tensor, torch_out_golden_tensor, pcc=0.998), e2e_perf] diff --git a/tests/sweep_framework/sweeps/conv2d/short/conv2d_short_sweep.py b/tests/sweep_framework/sweeps/conv2d/short/conv2d_short_sweep.py index 55fb094e4ec..91752c80ec1 100644 --- a/tests/sweep_framework/sweeps/conv2d/short/conv2d_short_sweep.py +++ b/tests/sweep_framework/sweeps/conv2d/short/conv2d_short_sweep.py @@ -22,1564 +22,1564 @@ "short_sweep_suite_conv2d": { "input_specs": [ # Contains following params - # [batch_size, output_channels, input_channels, input_height, input_width, kernel_height, kernel_width, stride_x, stride_y, pad_x, pad_y, groups, bias, dilation] - [1, 960, 960, 27, 27, 5, 5, 2, 2, 0, 0, 960, False, 1], - [1, 960, 960, 3, 3, 1, 5, 1, 1, 0, 2, 960, False, 1], - [1, 960, 960, 3, 3, 5, 1, 1, 1, 2, 0, 960, False, 1], - [1, 960, 960, 7, 7, 3, 3, 1, 1, 1, 1, 960, False, 1], - [1, 960, 960, 7, 7, 5, 5, 1, 1, 2, 2, 960, False, 1], - [1, 960, 960, 24, 24, 5, 5, 1, 1, 2, 2, 960, False, 1], - [1, 96, 96, 112, 112, 3, 3, 2, 2, 1, 1, 96, False, 1], - [1, 96, 96, 113, 113, 3, 3, 2, 2, 0, 0, 96, False, 1], - [1, 96, 96, 121, 121, 3, 3, 2, 2, 0, 0, 96, False, 1], - [1, 96, 96, 131, 131, 3, 3, 2, 2, 0, 0, 96, False, 1], - [1, 96, 96, 28, 28, 5, 5, 2, 2, 2, 2, 96, False, 1], - [1, 92, 92, 14, 14, 3, 3, 1, 1, 1, 1, 92, False, 1], - [1, 144, 144, 28, 28, 3, 3, 1, 1, 1, 1, 9, False, 1], - [1, 144, 144, 56, 56, 3, 3, 2, 2, 1, 1, 9, False, 1], - [1, 216, 216, 28, 28, 3, 3, 1, 1, 1, 1, 9, False, 1], - [1, 216, 216, 56, 56, 3, 3, 2, 2, 1, 1, 9, False, 1], - [1, 432, 432, 14, 14, 3, 3, 1, 1, 1, 1, 9, False, 1], - [1, 432, 432, 28, 28, 3, 3, 2, 2, 1, 1, 9, False, 1], - [1, 88, 88, 28, 28, 3, 3, 1, 1, 1, 1, 88, False, 1], - [1, 816, 816, 19, 19, 5, 5, 1, 1, 2, 2, 816, False, 1], - [1, 816, 816, 23, 23, 5, 5, 2, 2, 0, 0, 816, False, 1], - [1, 80, 80, 14, 14, 3, 3, 1, 1, 1, 1, 80, False, 1], - [1, 80, 80, 7, 7, 3, 3, 1, 1, 1, 1, 80, False, 1], - [1, 128, 128, 28, 28, 3, 3, 1, 1, 1, 1, 8, False, 1], - [1, 128, 128, 56, 56, 3, 3, 2, 2, 1, 1, 8, False, 1], - [1, 1344, 1344, 14, 14, 3, 3, 1, 1, 1, 1, 8, False, 1], - [1, 1344, 1344, 28, 28, 3, 3, 2, 2, 1, 1, 8, False, 1], - [1, 448, 448, 28, 28, 3, 3, 1, 1, 1, 1, 8, False, 1], - [1, 448, 448, 56, 56, 3, 3, 2, 2, 1, 1, 8, False, 1], - [1, 8, 8, 112, 112, 3, 3, 1, 1, 1, 1, 8, False, 1], - [1, 728, 728, 19, 19, 3, 3, 1, 1, 1, 1, 728, False, 1], - [1, 728, 728, 38, 38, 3, 3, 2, 2, 1, 1, 728, False, 1], - [1, 728, 728, 38, 38, 3, 3, 1, 1, 1, 1, 728, False, 1], - [1, 720, 720, 17, 17, 5, 5, 1, 1, 2, 2, 720, False, 1], - [1, 720, 720, 21, 21, 5, 5, 2, 2, 0, 0, 720, False, 1], - [1, 72, 72, 28, 28, 1, 5, 1, 1, 0, 2, 72, False, 1], - [1, 72, 72, 28, 28, 5, 1, 1, 1, 2, 0, 72, False, 1], - [1, 72, 72, 56, 56, 3, 3, 1, 1, 1, 1, 72, False, 1], - [1, 72, 72, 56, 56, 3, 3, 2, 2, 1, 1, 72, False, 1], - [1, 72, 72, 56, 56, 5, 5, 2, 2, 2, 2, 72, False, 1], - [1, 72, 72, 80, 80, 3, 3, 1, 1, 1, 1, 72, False, 1], - [1, 72, 72, 80, 80, 5, 5, 2, 2, 2, 2, 72, False, 1], - [1, 168, 168, 28, 28, 3, 3, 1, 1, 1, 1, 7, False, 1], - [1, 168, 168, 56, 56, 3, 3, 2, 2, 1, 1, 7, False, 1], - [1, 896, 896, 14, 14, 3, 3, 1, 1, 1, 1, 7, False, 1], - [1, 896, 896, 28, 28, 3, 3, 2, 2, 1, 1, 7, False, 1], - [1, 672, 672, 14, 14, 3, 3, 1, 1, 1, 1, 672, False, 1], - [1, 672, 672, 14, 14, 5, 5, 1, 1, 2, 2, 672, False, 1], - [1, 672, 672, 14, 14, 5, 5, 2, 2, 2, 2, 672, False, 1], - [1, 672, 672, 15, 15, 5, 5, 1, 1, 2, 2, 672, False, 1], - [1, 672, 672, 17, 17, 5, 5, 2, 2, 0, 0, 672, False, 1], - [1, 672, 672, 19, 19, 5, 5, 2, 2, 0, 0, 672, False, 1], - [1, 672, 672, 20, 20, 3, 3, 1, 1, 1, 1, 672, False, 1], - [1, 672, 672, 20, 20, 5, 5, 2, 2, 2, 2, 672, False, 1], - [1, 672, 672, 24, 24, 3, 3, 1, 1, 1, 1, 672, False, 1], - [1, 672, 672, 24, 24, 5, 5, 1, 1, 2, 2, 672, False, 1], - [1, 672, 672, 7, 7, 1, 5, 1, 1, 0, 2, 672, False, 1], - [1, 672, 672, 7, 7, 5, 1, 1, 1, 2, 0, 672, False, 1], - [1, 640, 640, 32, 32, 3, 3, 1, 1, 1, 1, 640, True, 1], - [1, 1024, 1024, 14, 14, 3, 3, 1, 1, 1, 1, 64, False, 1], - [1, 1024, 1024, 28, 28, 3, 3, 2, 2, 1, 1, 64, False, 1], - [1, 2048, 2048, 14, 14, 3, 3, 2, 2, 1, 1, 64, False, 1], - [1, 2048, 2048, 7, 7, 3, 3, 1, 1, 1, 1, 64, False, 1], - [1, 512, 512, 28, 28, 3, 3, 1, 1, 1, 1, 64, False, 1], - [1, 512, 512, 56, 56, 3, 3, 2, 2, 1, 1, 64, False, 1], - [1, 64, 64, 112, 112, 3, 3, 1, 1, 1, 1, 64, False, 1], - [1, 64, 64, 112, 112, 3, 3, 2, 2, 1, 1, 64, False, 1], - [1, 64, 64, 150, 150, 3, 3, 1, 1, 1, 1, 64, False, 1], - [1, 64, 64, 160, 160, 3, 3, 2, 2, 1, 1, 64, False, 1], - [1, 64, 64, 2, 2, 3, 3, 2, 2, 1, 1, 64, False, 1], - [1, 256, 256, 56, 56, 3, 3, 1, 1, 1, 1, 64, False, 1], - [1, 1512, 1512, 14, 14, 3, 3, 2, 2, 1, 1, 63, False, 1], - [1, 60, 60, 28, 28, 3, 3, 1, 1, 1, 1, 60, False, 1], - [1, 1392, 1392, 14, 14, 3, 3, 1, 1, 1, 1, 6, False, 1], - [1, 1392, 1392, 28, 28, 3, 3, 2, 2, 1, 1, 6, False, 1], - [1, 48, 48, 112, 112, 3, 3, 2, 2, 1, 1, 6, False, 1], - [1, 720, 720, 14, 14, 3, 3, 1, 1, 1, 1, 6, False, 1], - [1, 720, 720, 28, 28, 3, 3, 2, 2, 1, 1, 6, False, 1], - [1, 576, 576, 14, 14, 3, 3, 1, 1, 1, 1, 576, False, 1], - [1, 576, 576, 14, 14, 3, 3, 2, 2, 1, 1, 576, False, 1], - [1, 576, 576, 19, 19, 3, 3, 1, 1, 1, 1, 576, False, 1], - [1, 576, 576, 19, 19, 5, 5, 1, 1, 2, 2, 576, False, 1], - [1, 576, 576, 7, 7, 5, 5, 1, 1, 2, 2, 576, False, 1], - [1, 56, 56, 14, 14, 3, 3, 1, 1, 1, 1, 56, False, 1], - [1, 440, 440, 14, 14, 3, 3, 2, 2, 1, 1, 55, False, 1], - [1, 440, 440, 7, 7, 3, 3, 1, 1, 1, 1, 55, False, 1], - [1, 528, 528, 17, 17, 3, 3, 1, 1, 1, 1, 528, False, 1], - [1, 528, 528, 17, 17, 5, 5, 1, 1, 2, 2, 528, False, 1], - [1, 512, 512, 14, 14, 3, 3, 1, 1, 1, 1, 512, False, 1], - [1, 512, 512, 14, 14, 3, 3, 2, 2, 1, 1, 512, False, 1], - [1, 512, 512, 28, 28, 3, 3, 1, 1, 1, 1, 512, False, 1], - [1, 512, 512, 28, 28, 3, 3, 1, 1, 2, 2, 512, False, 1], - [1, 512, 512, 5, 5, 3, 3, 1, 1, 1, 1, 512, False, 1], - [1, 512, 512, 60, 80, 3, 3, 1, 1, 1, 1, 512, True, 1], - [1, 120, 120, 28, 28, 3, 3, 1, 1, 1, 1, 5, False, 1], - [1, 120, 120, 56, 56, 3, 3, 2, 2, 1, 1, 5, False, 1], - [1, 784, 784, 14, 14, 3, 3, 2, 2, 1, 1, 49, False, 1], - [1, 784, 784, 7, 7, 3, 3, 1, 1, 1, 1, 49, False, 1], - [1, 480, 480, 10, 10, 3, 3, 1, 1, 1, 1, 480, False, 1], - [1, 480, 480, 10, 10, 5, 5, 1, 1, 2, 2, 480, False, 1], - [1, 480, 480, 14, 14, 3, 3, 1, 1, 1, 1, 480, False, 1], - [1, 480, 480, 14, 14, 5, 5, 1, 1, 2, 2, 480, False, 1], - [1, 480, 480, 15, 15, 3, 3, 1, 1, 1, 1, 480, False, 1], - [1, 480, 480, 15, 15, 5, 5, 1, 1, 2, 2, 480, False, 1], - [1, 480, 480, 20, 20, 3, 3, 1, 1, 1, 1, 480, False, 1], - [1, 480, 480, 7, 7, 1, 5, 1, 1, 0, 2, 480, False, 1], - [1, 480, 480, 7, 7, 3, 3, 1, 1, 1, 1, 480, False, 1], - [1, 480, 480, 7, 7, 5, 1, 1, 1, 2, 0, 480, False, 1], - [1, 48, 48, 112, 112, 3, 3, 2, 2, 1, 1, 48, False, 1], - [1, 672, 672, 14, 14, 3, 3, 2, 2, 1, 1, 42, False, 1], - [1, 672, 672, 7, 7, 3, 3, 1, 1, 1, 1, 42, False, 1], - [1, 40, 40, 14, 14, 3, 3, 1, 1, 1, 1, 40, False, 1], - [1, 40, 40, 28, 28, 3, 3, 2, 2, 1, 1, 40, False, 1], - [1, 192, 192, 28, 28, 3, 3, 1, 1, 1, 1, 4, False, 1], - [1, 192, 192, 56, 56, 3, 3, 2, 2, 1, 1, 4, False, 1], - [1, 224, 224, 112, 112, 3, 3, 2, 2, 1, 1, 4, False, 1], - [1, 224, 224, 56, 56, 3, 3, 1, 1, 1, 1, 4, False, 1], - [1, 448, 448, 28, 28, 3, 3, 1, 1, 1, 1, 4, False, 1], - [1, 448, 448, 56, 56, 3, 3, 2, 2, 1, 1, 4, False, 1], - [1, 512, 512, 28, 28, 3, 3, 1, 1, 1, 1, 4, False, 1], - [1, 512, 512, 56, 56, 3, 3, 2, 2, 1, 1, 4, False, 1], - [1, 64, 64, 112, 112, 3, 3, 2, 2, 1, 1, 4, False, 1], - [1, 64, 64, 28, 28, 3, 3, 1, 1, 1, 1, 4, False, 1], - [1, 64, 64, 56, 56, 3, 3, 2, 2, 1, 1, 4, False, 1], - [1, 672, 672, 28, 28, 3, 3, 1, 1, 1, 1, 4, False, 1], - [1, 672, 672, 56, 56, 3, 3, 2, 2, 1, 1, 4, False, 1], - [1, 1056, 1056, 48, 48, 3, 3, 1, 1, 1, 1, 4, False, 1], - [1, 1056, 1056, 96, 96, 3, 3, 2, 2, 1, 1, 4, False, 1], - [1, 384, 384, 14, 14, 3, 3, 1, 1, 1, 1, 384, False, 1], - [1, 912, 912, 14, 14, 3, 3, 2, 2, 1, 1, 38, False, 1], - [1, 912, 912, 7, 7, 3, 3, 1, 1, 1, 1, 38, False, 1], - [1, 888, 888, 14, 14, 3, 3, 2, 2, 1, 1, 37, False, 1], - [1, 888, 888, 7, 7, 3, 3, 1, 1, 1, 1, 37, False, 1], - [1, 2016, 2016, 14, 14, 3, 3, 2, 2, 1, 1, 36, False, 1], - [1, 36, 36, 56, 56, 3, 3, 1, 1, 1, 1, 36, False, 1], - [1, 336, 336, 14, 14, 3, 3, 1, 1, 1, 1, 336, False, 1], - [1, 336, 336, 49, 49, 3, 3, 2, 2, 0, 0, 336, False, 1], - [1, 336, 336, 48, 48, 5, 5, 1, 1, 2, 2, 336, False, 1], - [1, 1024, 1024, 14, 14, 3, 3, 1, 1, 1, 1, 32, False, 1], - [1, 1024, 1024, 14, 14, 3, 3, 2, 2, 1, 1, 32, False, 1], - [1, 1024, 1024, 28, 28, 3, 3, 2, 2, 1, 1, 32, False, 1], - [1, 1024, 1024, 7, 7, 3, 3, 1, 1, 1, 1, 32, False, 1], - [1, 128, 128, 56, 56, 3, 3, 1, 1, 1, 1, 32, False, 1], - [1, 2048, 2048, 14, 14, 3, 3, 2, 2, 1, 1, 32, False, 1], - [1, 2048, 2048, 7, 7, 3, 3, 1, 1, 1, 1, 32, False, 1], - [1, 256, 256, 28, 28, 3, 3, 1, 1, 1, 1, 32, False, 1], - [1, 256, 256, 56, 56, 3, 3, 2, 2, 1, 1, 32, False, 1], - [1, 32, 32, 112, 112, 3, 3, 1, 1, 1, 1, 32, False, 1], - [1, 32, 32, 120, 120, 3, 3, 1, 1, 1, 1, 32, False, 1], - [1, 32, 32, 130, 130, 3, 3, 1, 1, 1, 1, 32, False, 1], - [1, 32, 32, 150, 150, 3, 3, 1, 1, 1, 1, 32, False, 1], - [1, 32, 32, 190, 190, 3, 3, 1, 1, 1, 1, 32, False, 1], - [1, 512, 512, 14, 14, 3, 3, 1, 1, 1, 1, 32, False, 1], - [1, 512, 512, 28, 28, 3, 3, 1, 1, 1, 1, 32, False, 1], - [1, 512, 512, 28, 28, 3, 3, 2, 2, 1, 1, 32, False, 1], - [1, 512, 512, 56, 56, 3, 3, 2, 2, 1, 1, 32, False, 1], - [1, 256, 256, 56, 56, 3, 3, 1, 1, 1, 1, 32, False, 1], - [1, 72, 72, 112, 112, 3, 3, 2, 2, 1, 1, 3, False, 1], - [1, 72, 72, 56, 56, 3, 3, 1, 1, 1, 1, 3, False, 1], - [1, 696, 696, 28, 28, 3, 3, 1, 1, 1, 1, 3, False, 1], - [1, 696, 696, 56, 56, 3, 3, 2, 2, 1, 1, 3, False, 1], - [1, 288, 288, 14, 14, 5, 5, 2, 2, 2, 2, 288, False, 1], - [1, 288, 288, 33, 33, 5, 5, 1, 1, 2, 2, 288, False, 1], - [1, 288, 288, 35, 35, 3, 3, 2, 2, 0, 0, 288, False, 1], - [1, 288, 288, 38, 38, 5, 5, 1, 1, 2, 2, 288, False, 1], - [1, 288, 288, 39, 39, 3, 3, 2, 2, 0, 0, 288, False, 1], - [1, 7392, 7392, 24, 24, 3, 3, 2, 2, 1, 1, 28, False, 1], - [1, 3024, 3024, 14, 14, 3, 3, 2, 2, 1, 1, 27, False, 1], - [1, 208, 208, 14, 14, 3, 3, 1, 1, 1, 1, 26, False, 1], - [1, 208, 208, 28, 28, 3, 3, 2, 2, 1, 1, 26, False, 1], - [1, 256, 256, 10, 10, 3, 3, 2, 2, 1, 1, 256, False, 1], - [1, 256, 256, 2, 2, 3, 3, 1, 1, 1, 1, 256, False, 1], - [1, 256, 256, 28, 28, 3, 3, 1, 1, 1, 1, 256, False, 1], - [1, 256, 256, 28, 28, 3, 3, 2, 2, 1, 1, 256, False, 1], - [1, 256, 256, 3, 3, 3, 3, 1, 1, 1, 1, 256, False, 1], - [1, 256, 256, 38, 38, 3, 3, 1, 1, 1, 1, 256, False, 1], - [1, 256, 256, 64, 64, 3, 3, 1, 1, 1, 1, 256, True, 1], - [1, 256, 256, 75, 75, 3, 3, 2, 2, 1, 1, 256, False, 1], - [1, 256, 256, 120, 160, 3, 3, 1, 1, 1, 1, 256, True, 1], - [1, 256, 256, 75, 75, 3, 3, 1, 1, 1, 1, 256, False, 1], - 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0, 1, True, 1], - [1, 32, 3, 299, 299, 3, 3, 2, 2, 0, 0, 1, False, 1], - [1, 32, 3, 381, 381, 3, 3, 2, 2, 0, 0, 1, False, 1], - [1, 768, 3, 384, 512, 32, 32, 32, 32, 0, 0, 1, True, 1], - [1, 64, 3, 480, 640, 7, 7, 4, 4, 3, 3, 1, True, 1], - [1, 32, 3, 512, 512, 3, 3, 1, 1, 1, 1, 1, False, 1], - [1, 32, 3, 512, 512, 6, 6, 2, 2, 2, 2, 1, False, 1], - [1, 32, 3, 512, 512, 7, 7, 4, 4, 3, 3, 1, True, 1], - [1, 192, 3, 512, 672, 16, 16, 16, 16, 0, 0, 1, True, 1], - [1, 1280, 3, 518, 518, 14, 14, 14, 14, 0, 0, 1, True, 1], - [1, 64, 3, 720, 1280, 7, 7, 2, 2, 3, 3, 1, False, 1], - [1, 64, 3, 800, 1088, 7, 7, 2, 2, 3, 3, 1, False, 1], - [1, 308, 3024, 1, 1, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 3024, 3024, 7, 7, 1, 1, 1, 1, 0, 0, 1, False, 1], - [1, 3024, 308, 1, 1, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 528, 32, 192, 192, 1, 1, 1, 1, 0, 0, 1, False, 1], - [1, 64, 32, 512, 512, 3, 3, 2, 2, 1, 1, 1, False, 1], - [1, 888, 336, 14, 14, 1, 1, 2, 2, 0, 0, 1, False, 1], - [1, 3712, 348, 1, 1, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 348, 3712, 1, 1, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 3712, 3712, 7, 7, 1, 1, 1, 1, 0, 0, 1, False, 1], - [1, 912, 408, 14, 14, 1, 1, 2, 2, 0, 0, 1, False, 1], - [1, 1008, 432, 14, 14, 1, 1, 2, 2, 0, 0, 1, False, 1], - [1, 128, 512, 100, 136, 1, 1, 1, 1, 0, 0, 1, False, 1], - [1, 256, 512, 100, 136, 1, 1, 1, 1, 0, 0, 1, False, 1], - [1, 256, 512, 100, 136, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 364, 512, 38, 38, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 512, 512, 38, 38, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 256, 512, 56, 56, 3, 3, 1, 1, 1, 1, 1, False, 1], - [1, 1024, 512, 90, 160, 1, 1, 2, 2, 0, 0, 1, False, 1], - [1, 1056, 528, 96, 96, 1, 1, 1, 1, 0, 0, 1, False, 1], - [1, 1056, 528, 96, 96, 1, 1, 2, 2, 0, 0, 1, False, 1], - [1, 64, 64, 128, 128, 2, 2, 2, 2, 0, 0, 1, True, 1], - [1, 256, 64, 180, 320, 1, 1, 1, 1, 0, 0, 1, False, 1], - [1, 1, 64, 480, 640, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 64, 64, 480, 640, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 1392, 696, 28, 28, 1, 1, 1, 1, 0, 0, 1, False, 1], - [1, 1392, 696, 28, 28, 1, 1, 2, 2, 0, 0, 1, False, 1], - [1, 1920, 720, 14, 14, 1, 1, 1, 1, 0, 0, 1, False, 1], - [1, 1920, 720, 14, 14, 1, 1, 2, 2, 0, 0, 1, False, 1], - [1, 2904, 726, 1, 1, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 7392, 726, 1, 1, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 1024, 728, 19, 19, 1, 1, 1, 1, 0, 0, 1, False, 1], - [1, 1024, 728, 19, 19, 1, 1, 2, 2, 0, 0, 1, False, 1], - [1, 728, 728, 38, 38, 1, 1, 2, 2, 0, 0, 1, False, 1], - [1, 726, 7392, 1, 1, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 7392, 7392, 12, 12, 1, 1, 1, 1, 0, 0, 1, False, 1], - [1, 1024, 782, 7, 7, 1, 1, 1, 1, 0, 0, 1, False, 1], - [1, 912, 912, 7, 7, 1, 1, 1, 1, 0, 0, 1, False, 1], - [1, 1280, 960, 1, 1, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 1280, 1280, 16, 16, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 1280, 1280, 16, 16, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 1280, 1280, 16, 16, 3, 3, 2, 2, 1, 1, 1, True, 1], - [1, 1280, 1280, 32, 32, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 640, 1280, 32, 32, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 640, 1280, 32, 32, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 1280, 1280, 8, 8, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 1280, 1280, 8, 8, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 1280, 1920, 16, 16, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 1280, 1920, 16, 16, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 640, 1920, 32, 32, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 640, 1920, 32, 32, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 1280, 2560, 16, 16, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 1280, 2560, 16, 16, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 1280, 2560, 8, 8, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 1280, 2560, 8, 8, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 640, 320, 32, 32, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 640, 320, 32, 32, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 320, 320, 64, 64, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 320, 320, 64, 64, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 320, 320, 64, 64, 3, 3, 2, 2, 1, 1, 1, True, 1], - [1, 4, 320, 64, 64, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 320, 4, 64, 64, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 1280, 640, 16, 16, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 1280, 640, 16, 16, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 640, 640, 32, 32, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 640, 640, 32, 32, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 640, 640, 32, 32, 3, 3, 2, 2, 1, 1, 1, True, 1], - [1, 320, 640, 64, 64, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 320, 640, 64, 64, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 640, 640, 64, 64, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 640, 960, 32, 32, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 640, 960, 32, 32, 3, 3, 1, 1, 1, 1, 1, True, 1], - [1, 320, 960, 64, 64, 1, 1, 1, 1, 0, 0, 1, True, 1], - [1, 320, 960, 64, 64, 3, 3, 1, 1, 1, 1, 1, True, 1], + # [batch_size, output_channels, input_channels, input_height, input_width, kernel_height, kernel_width, stride_h, stride_w, pad_h, pad_w, groups, dilation_h, dilation_w, bias] + [1, 960, 960, 27, 27, 5, 5, 2, 2, 0, 0, 960, 1, 1, False], + [1, 960, 960, 3, 3, 1, 5, 1, 1, 0, 2, 960, 1, 1, False], + [1, 960, 960, 3, 3, 5, 1, 1, 1, 2, 0, 960, 1, 1, False], + [1, 960, 960, 7, 7, 3, 3, 1, 1, 1, 1, 960, 1, 1, False], + [1, 960, 960, 7, 7, 5, 5, 1, 1, 2, 2, 960, 1, 1, False], + [1, 960, 960, 24, 24, 5, 5, 1, 1, 2, 2, 960, 1, 1, False], + [1, 96, 96, 112, 112, 3, 3, 2, 2, 1, 1, 96, 1, 1, False], + [1, 96, 96, 113, 113, 3, 3, 2, 2, 0, 0, 96, 1, 1, False], + [1, 96, 96, 121, 121, 3, 3, 2, 2, 0, 0, 96, 1, 1, False], + [1, 96, 96, 131, 131, 3, 3, 2, 2, 0, 0, 96, 1, 1, False], + [1, 96, 96, 28, 28, 5, 5, 2, 2, 2, 2, 96, 1, 1, False], + [1, 92, 92, 14, 14, 3, 3, 1, 1, 1, 1, 92, 1, 1, False], + [1, 144, 144, 28, 28, 3, 3, 1, 1, 1, 1, 9, 1, 1, False], + [1, 144, 144, 56, 56, 3, 3, 2, 2, 1, 1, 9, 1, 1, False], + [1, 216, 216, 28, 28, 3, 3, 1, 1, 1, 1, 9, 1, 1, False], + [1, 216, 216, 56, 56, 3, 3, 2, 2, 1, 1, 9, 1, 1, False], + [1, 432, 432, 14, 14, 3, 3, 1, 1, 1, 1, 9, 1, 1, False], + [1, 432, 432, 28, 28, 3, 3, 2, 2, 1, 1, 9, 1, 1, False], + [1, 88, 88, 28, 28, 3, 3, 1, 1, 1, 1, 88, 1, 1, False], + [1, 816, 816, 19, 19, 5, 5, 1, 1, 2, 2, 816, 1, 1, False], + [1, 816, 816, 23, 23, 5, 5, 2, 2, 0, 0, 816, 1, 1, False], + [1, 80, 80, 14, 14, 3, 3, 1, 1, 1, 1, 80, 1, 1, False], + [1, 80, 80, 7, 7, 3, 3, 1, 1, 1, 1, 80, 1, 1, False], + [1, 128, 128, 28, 28, 3, 3, 1, 1, 1, 1, 8, 1, 1, False], + [1, 128, 128, 56, 56, 3, 3, 2, 2, 1, 1, 8, 1, 1, False], + [1, 1344, 1344, 14, 14, 3, 3, 1, 1, 1, 1, 8, 1, 1, False], + [1, 1344, 1344, 28, 28, 3, 3, 2, 2, 1, 1, 8, 1, 1, False], + [1, 448, 448, 28, 28, 3, 3, 1, 1, 1, 1, 8, 1, 1, False], + [1, 448, 448, 56, 56, 3, 3, 2, 2, 1, 1, 8, 1, 1, False], + [1, 8, 8, 112, 112, 3, 3, 1, 1, 1, 1, 8, 1, 1, False], + [1, 728, 728, 19, 19, 3, 3, 1, 1, 1, 1, 728, 1, 1, False], + [1, 728, 728, 38, 38, 3, 3, 2, 2, 1, 1, 728, 1, 1, False], + [1, 728, 728, 38, 38, 3, 3, 1, 1, 1, 1, 728, 1, 1, False], + [1, 720, 720, 17, 17, 5, 5, 1, 1, 2, 2, 720, 1, 1, False], + [1, 720, 720, 21, 21, 5, 5, 2, 2, 0, 0, 720, 1, 1, False], + [1, 72, 72, 28, 28, 1, 5, 1, 1, 0, 2, 72, 1, 1, False], + [1, 72, 72, 28, 28, 5, 1, 1, 1, 2, 0, 72, 1, 1, False], + [1, 72, 72, 56, 56, 3, 3, 1, 1, 1, 1, 72, 1, 1, False], + [1, 72, 72, 56, 56, 3, 3, 2, 2, 1, 1, 72, 1, 1, False], + [1, 72, 72, 56, 56, 5, 5, 2, 2, 2, 2, 72, 1, 1, False], + [1, 72, 72, 80, 80, 3, 3, 1, 1, 1, 1, 72, 1, 1, False], + [1, 72, 72, 80, 80, 5, 5, 2, 2, 2, 2, 72, 1, 1, False], + [1, 168, 168, 28, 28, 3, 3, 1, 1, 1, 1, 7, 1, 1, False], + [1, 168, 168, 56, 56, 3, 3, 2, 2, 1, 1, 7, 1, 1, False], + [1, 896, 896, 14, 14, 3, 3, 1, 1, 1, 1, 7, 1, 1, False], + [1, 896, 896, 28, 28, 3, 3, 2, 2, 1, 1, 7, 1, 1, False], + [1, 672, 672, 14, 14, 3, 3, 1, 1, 1, 1, 672, 1, 1, False], + [1, 672, 672, 14, 14, 5, 5, 1, 1, 2, 2, 672, 1, 1, False], + [1, 672, 672, 14, 14, 5, 5, 2, 2, 2, 2, 672, 1, 1, False], + [1, 672, 672, 15, 15, 5, 5, 1, 1, 2, 2, 672, 1, 1, False], + [1, 672, 672, 17, 17, 5, 5, 2, 2, 0, 0, 672, 1, 1, False], + [1, 672, 672, 19, 19, 5, 5, 2, 2, 0, 0, 672, 1, 1, False], + [1, 672, 672, 20, 20, 3, 3, 1, 1, 1, 1, 672, 1, 1, False], + [1, 672, 672, 20, 20, 5, 5, 2, 2, 2, 2, 672, 1, 1, False], + [1, 672, 672, 24, 24, 3, 3, 1, 1, 1, 1, 672, 1, 1, False], + [1, 672, 672, 24, 24, 5, 5, 1, 1, 2, 2, 672, 1, 1, False], + [1, 672, 672, 7, 7, 1, 5, 1, 1, 0, 2, 672, 1, 1, False], + [1, 672, 672, 7, 7, 5, 1, 1, 1, 2, 0, 672, 1, 1, False], + [1, 640, 640, 32, 32, 3, 3, 1, 1, 1, 1, 640, 1, 1, True], + [1, 1024, 1024, 14, 14, 3, 3, 1, 1, 1, 1, 64, 1, 1, False], + [1, 1024, 1024, 28, 28, 3, 3, 2, 2, 1, 1, 64, 1, 1, False], + [1, 2048, 2048, 14, 14, 3, 3, 2, 2, 1, 1, 64, 1, 1, False], + [1, 2048, 2048, 7, 7, 3, 3, 1, 1, 1, 1, 64, 1, 1, False], + [1, 512, 512, 28, 28, 3, 3, 1, 1, 1, 1, 64, 1, 1, False], + [1, 512, 512, 56, 56, 3, 3, 2, 2, 1, 1, 64, 1, 1, False], + [1, 64, 64, 112, 112, 3, 3, 1, 1, 1, 1, 64, 1, 1, False], + [1, 64, 64, 112, 112, 3, 3, 2, 2, 1, 1, 64, 1, 1, False], + [1, 64, 64, 150, 150, 3, 3, 1, 1, 1, 1, 64, 1, 1, False], + [1, 64, 64, 160, 160, 3, 3, 2, 2, 1, 1, 64, 1, 1, False], + [1, 64, 64, 2, 2, 3, 3, 2, 2, 1, 1, 64, 1, 1, False], + [1, 256, 256, 56, 56, 3, 3, 1, 1, 1, 1, 64, 1, 1, False], + [1, 1512, 1512, 14, 14, 3, 3, 2, 2, 1, 1, 63, 1, 1, False], + [1, 60, 60, 28, 28, 3, 3, 1, 1, 1, 1, 60, 1, 1, False], + [1, 1392, 1392, 14, 14, 3, 3, 1, 1, 1, 1, 6, 1, 1, False], + [1, 1392, 1392, 28, 28, 3, 3, 2, 2, 1, 1, 6, 1, 1, False], + [1, 48, 48, 112, 112, 3, 3, 2, 2, 1, 1, 6, 1, 1, False], + [1, 720, 720, 14, 14, 3, 3, 1, 1, 1, 1, 6, 1, 1, False], + [1, 720, 720, 28, 28, 3, 3, 2, 2, 1, 1, 6, 1, 1, False], + [1, 576, 576, 14, 14, 3, 3, 1, 1, 1, 1, 576, 1, 1, False], + [1, 576, 576, 14, 14, 3, 3, 2, 2, 1, 1, 576, 1, 1, False], + [1, 576, 576, 19, 19, 3, 3, 1, 1, 1, 1, 576, 1, 1, False], + [1, 576, 576, 19, 19, 5, 5, 1, 1, 2, 2, 576, 1, 1, False], + [1, 576, 576, 7, 7, 5, 5, 1, 1, 2, 2, 576, 1, 1, False], + [1, 56, 56, 14, 14, 3, 3, 1, 1, 1, 1, 56, 1, 1, False], + [1, 440, 440, 14, 14, 3, 3, 2, 2, 1, 1, 55, 1, 1, False], + [1, 440, 440, 7, 7, 3, 3, 1, 1, 1, 1, 55, 1, 1, False], + [1, 528, 528, 17, 17, 3, 3, 1, 1, 1, 1, 528, 1, 1, False], + [1, 528, 528, 17, 17, 5, 5, 1, 1, 2, 2, 528, 1, 1, False], + [1, 512, 512, 14, 14, 3, 3, 1, 1, 1, 1, 512, 1, 1, False], + [1, 512, 512, 14, 14, 3, 3, 2, 2, 1, 1, 512, 1, 1, False], + [1, 512, 512, 28, 28, 3, 3, 1, 1, 1, 1, 512, 1, 1, False], + [1, 512, 512, 28, 28, 3, 3, 1, 1, 2, 2, 512, 1, 1, False], + [1, 512, 512, 5, 5, 3, 3, 1, 1, 1, 1, 512, 1, 1, False], + [1, 512, 512, 60, 80, 3, 3, 1, 1, 1, 1, 512, 1, 1, True], + [1, 120, 120, 28, 28, 3, 3, 1, 1, 1, 1, 5, 1, 1, False], + [1, 120, 120, 56, 56, 3, 3, 2, 2, 1, 1, 5, 1, 1, False], + [1, 784, 784, 14, 14, 3, 3, 2, 2, 1, 1, 49, 1, 1, False], + [1, 784, 784, 7, 7, 3, 3, 1, 1, 1, 1, 49, 1, 1, False], + [1, 480, 480, 10, 10, 3, 3, 1, 1, 1, 1, 480, 1, 1, False], + [1, 480, 480, 10, 10, 5, 5, 1, 1, 2, 2, 480, 1, 1, False], + [1, 480, 480, 14, 14, 3, 3, 1, 1, 1, 1, 480, 1, 1, False], + [1, 480, 480, 14, 14, 5, 5, 1, 1, 2, 2, 480, 1, 1, False], + [1, 480, 480, 15, 15, 3, 3, 1, 1, 1, 1, 480, 1, 1, False], + [1, 480, 480, 15, 15, 5, 5, 1, 1, 2, 2, 480, 1, 1, False], + [1, 480, 480, 20, 20, 3, 3, 1, 1, 1, 1, 480, 1, 1, False], + [1, 480, 480, 7, 7, 1, 5, 1, 1, 0, 2, 480, 1, 1, False], + [1, 480, 480, 7, 7, 3, 3, 1, 1, 1, 1, 480, 1, 1, False], + [1, 480, 480, 7, 7, 5, 1, 1, 1, 2, 0, 480, 1, 1, False], + [1, 48, 48, 112, 112, 3, 3, 2, 2, 1, 1, 48, 1, 1, False], + [1, 672, 672, 14, 14, 3, 3, 2, 2, 1, 1, 42, 1, 1, False], + [1, 672, 672, 7, 7, 3, 3, 1, 1, 1, 1, 42, 1, 1, False], + [1, 40, 40, 14, 14, 3, 3, 1, 1, 1, 1, 40, 1, 1, False], + [1, 40, 40, 28, 28, 3, 3, 2, 2, 1, 1, 40, 1, 1, False], + [1, 192, 192, 28, 28, 3, 3, 1, 1, 1, 1, 4, 1, 1, False], + [1, 192, 192, 56, 56, 3, 3, 2, 2, 1, 1, 4, 1, 1, False], + [1, 224, 224, 112, 112, 3, 3, 2, 2, 1, 1, 4, 1, 1, False], + [1, 224, 224, 56, 56, 3, 3, 1, 1, 1, 1, 4, 1, 1, False], + [1, 448, 448, 28, 28, 3, 3, 1, 1, 1, 1, 4, 1, 1, False], + [1, 448, 448, 56, 56, 3, 3, 2, 2, 1, 1, 4, 1, 1, False], + [1, 512, 512, 28, 28, 3, 3, 1, 1, 1, 1, 4, 1, 1, False], + [1, 512, 512, 56, 56, 3, 3, 2, 2, 1, 1, 4, 1, 1, False], + [1, 64, 64, 112, 112, 3, 3, 2, 2, 1, 1, 4, 1, 1, False], + [1, 64, 64, 28, 28, 3, 3, 1, 1, 1, 1, 4, 1, 1, False], + [1, 64, 64, 56, 56, 3, 3, 2, 2, 1, 1, 4, 1, 1, False], + [1, 672, 672, 28, 28, 3, 3, 1, 1, 1, 1, 4, 1, 1, False], + [1, 672, 672, 56, 56, 3, 3, 2, 2, 1, 1, 4, 1, 1, False], + [1, 1056, 1056, 48, 48, 3, 3, 1, 1, 1, 1, 4, 1, 1, False], + [1, 1056, 1056, 96, 96, 3, 3, 2, 2, 1, 1, 4, 1, 1, False], + [1, 384, 384, 14, 14, 3, 3, 1, 1, 1, 1, 384, 1, 1, False], + [1, 912, 912, 14, 14, 3, 3, 2, 2, 1, 1, 38, 1, 1, False], + [1, 912, 912, 7, 7, 3, 3, 1, 1, 1, 1, 38, 1, 1, False], + [1, 888, 888, 14, 14, 3, 3, 2, 2, 1, 1, 37, 1, 1, False], + [1, 888, 888, 7, 7, 3, 3, 1, 1, 1, 1, 37, 1, 1, False], + [1, 2016, 2016, 14, 14, 3, 3, 2, 2, 1, 1, 36, 1, 1, False], + [1, 36, 36, 56, 56, 3, 3, 1, 1, 1, 1, 36, 1, 1, False], + [1, 336, 336, 14, 14, 3, 3, 1, 1, 1, 1, 336, 1, 1, False], + [1, 336, 336, 49, 49, 3, 3, 2, 2, 0, 0, 336, 1, 1, False], + [1, 336, 336, 48, 48, 5, 5, 1, 1, 2, 2, 336, 1, 1, False], + [1, 1024, 1024, 14, 14, 3, 3, 1, 1, 1, 1, 32, 1, 1, False], + [1, 1024, 1024, 14, 14, 3, 3, 2, 2, 1, 1, 32, 1, 1, False], + [1, 1024, 1024, 28, 28, 3, 3, 2, 2, 1, 1, 32, 1, 1, False], + [1, 1024, 1024, 7, 7, 3, 3, 1, 1, 1, 1, 32, 1, 1, False], + [1, 128, 128, 56, 56, 3, 3, 1, 1, 1, 1, 32, 1, 1, False], + [1, 2048, 2048, 14, 14, 3, 3, 2, 2, 1, 1, 32, 1, 1, False], + [1, 2048, 2048, 7, 7, 3, 3, 1, 1, 1, 1, 32, 1, 1, False], + [1, 256, 256, 28, 28, 3, 3, 1, 1, 1, 1, 32, 1, 1, False], + [1, 256, 256, 56, 56, 3, 3, 2, 2, 1, 1, 32, 1, 1, False], + [1, 32, 32, 112, 112, 3, 3, 1, 1, 1, 1, 32, 1, 1, False], + [1, 32, 32, 120, 120, 3, 3, 1, 1, 1, 1, 32, 1, 1, False], + [1, 32, 32, 130, 130, 3, 3, 1, 1, 1, 1, 32, 1, 1, False], + [1, 32, 32, 150, 150, 3, 3, 1, 1, 1, 1, 32, 1, 1, False], + [1, 32, 32, 190, 190, 3, 3, 1, 1, 1, 1, 32, 1, 1, False], + [1, 512, 512, 14, 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14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 2016, 896, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 2016, 896, 14, 14, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 2048, 896, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 2048, 896, 14, 14, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 256, 896, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 896, 896, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 128, 896, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 192, 912, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 128, 928, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 128, 928, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 28, 94, 28, 28, 3, 3, 1, 1, 1, 1, 1, 1, 1, False], + [1, 24, 96, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 208, 96, 14, 14, 3, 3, 1, 1, 1, 1, 1, 1, 1, False], + [1, 40, 96, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 576, 96, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 576, 96, 19, 19, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 128, 96, 28, 28, 3, 3, 1, 1, 1, 1, 1, 1, 1, False], + [1, 96, 96, 35, 35, 3, 3, 1, 1, 1, 1, 1, 1, 1, False], + [1, 128, 96, 56, 56, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 192, 96, 56, 56, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 192, 96, 56, 56, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 24, 96, 56, 56, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 96, 96, 56, 56, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 24, 96, 60, 60, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 24, 96, 65, 65, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 576, 96, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 240, 960, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 272, 960, 12, 12, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 128, 960, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 192, 960, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 160, 960, 24, 24, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 128, 960, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 160, 960, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 320, 960, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 80, 960, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 20, 98, 28, 28, 3, 3, 1, 1, 1, 1, 1, 1, 1, False], + [1, 128, 992, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 128, 992, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 1024, 1024, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 256, 1024, 128, 128, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 1024, 1024, 14, 14, 2, 2, 2, 2, 0, 0, 1, 1, 1, True], + [1, 2048, 1024, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 2048, 1024, 14, 14, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 512, 1024, 28, 28, 3, 3, 1, 1, 1, 1, 1, 1, 1, False], + [1, 2048, 1024, 45, 80, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 2048, 1024, 50, 68, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 2048, 1024, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 2048, 1024, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 1056, 1056, 48, 48, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 2904, 1056, 48, 48, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 2904, 1056, 48, 48, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 3024, 1232, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 3024, 1232, 14, 14, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 128, 128, 112, 112, 2, 2, 2, 2, 0, 0, 1, 1, 1, True], + [1, 2520, 1344, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 2520, 1344, 14, 14, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 3712, 1392, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 3712, 1392, 14, 14, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 1024, 1440, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 1512, 1512, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 1536, 1536, 10, 10, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 2048, 1536, 10, 10, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 448, 1632, 12, 12, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 1920, 1920, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 2016, 2016, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 1024, 2048, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 2048, 2048, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 1056, 2112, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 576, 216, 28, 28, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 232, 232, 112, 112, 3, 3, 2, 2, 1, 1, 1, 1, 1, False], + [1, 232, 232, 56, 56, 3, 3, 1, 1, 1, 1, 1, 1, 1, False], + [1, 144, 24, 190, 190, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 720, 240, 28, 28, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 2520, 2520, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 819, 256, 100, 136, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 512, 256, 180, 320, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 512, 256, 200, 272, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 819, 256, 25, 34, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 819, 256, 50, 68, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 256, 256, 56, 56, 3, 3, 1, 1, 1, 1, 1, 1, 1, False], + [1, 256, 256, 56, 56, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 256, 256, 75, 75, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 2904, 264, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 264, 2904, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 726, 2904, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 2904, 2904, 24, 24, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 7392, 2904, 24, 24, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 7392, 2904, 24, 24, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 1024, 3, 224, 224, 16, 16, 16, 16, 0, 0, 1, 1, 1, True], + [1, 1024, 3, 224, 224, 32, 32, 32, 32, 0, 0, 1, 1, 1, True], + [1, 768, 3, 224, 224, 16, 16, 16, 16, 0, 0, 1, 1, 1, True], + [1, 768, 3, 224, 224, 32, 32, 32, 32, 0, 0, 1, 1, 1, False], + [1, 768, 3, 224, 224, 32, 32, 32, 32, 0, 0, 1, 1, 1, True], + [1, 32, 3, 299, 299, 3, 3, 2, 2, 0, 0, 1, 1, 1, False], + [1, 32, 3, 381, 381, 3, 3, 2, 2, 0, 0, 1, 1, 1, False], + [1, 768, 3, 384, 512, 32, 32, 32, 32, 0, 0, 1, 1, 1, True], + [1, 64, 3, 480, 640, 7, 7, 4, 4, 3, 3, 1, 1, 1, True], + [1, 32, 3, 512, 512, 3, 3, 1, 1, 1, 1, 1, 1, 1, False], + [1, 32, 3, 512, 512, 6, 6, 2, 2, 2, 2, 1, 1, 1, False], + [1, 32, 3, 512, 512, 7, 7, 4, 4, 3, 3, 1, 1, 1, True], + [1, 192, 3, 512, 672, 16, 16, 16, 16, 0, 0, 1, 1, 1, True], + [1, 1280, 3, 518, 518, 14, 14, 14, 14, 0, 0, 1, 1, 1, True], + [1, 64, 3, 720, 1280, 7, 7, 2, 2, 3, 3, 1, 1, 1, False], + [1, 64, 3, 800, 1088, 7, 7, 2, 2, 3, 3, 1, 1, 1, False], + [1, 308, 3024, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 3024, 3024, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 3024, 308, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 528, 32, 192, 192, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 64, 32, 512, 512, 3, 3, 2, 2, 1, 1, 1, 1, 1, False], + [1, 888, 336, 14, 14, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 3712, 348, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 348, 3712, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 3712, 3712, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 912, 408, 14, 14, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 1008, 432, 14, 14, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 128, 512, 100, 136, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 256, 512, 100, 136, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 256, 512, 100, 136, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 364, 512, 38, 38, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 512, 512, 38, 38, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 256, 512, 56, 56, 3, 3, 1, 1, 1, 1, 1, 1, 1, False], + [1, 1024, 512, 90, 160, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 1056, 528, 96, 96, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 1056, 528, 96, 96, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 64, 64, 128, 128, 2, 2, 2, 2, 0, 0, 1, 1, 1, True], + [1, 256, 64, 180, 320, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 1, 64, 480, 640, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 64, 64, 480, 640, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 1392, 696, 28, 28, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 1392, 696, 28, 28, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 1920, 720, 14, 14, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 1920, 720, 14, 14, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 2904, 726, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 7392, 726, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 1024, 728, 19, 19, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 1024, 728, 19, 19, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 728, 728, 38, 38, 1, 1, 2, 2, 0, 0, 1, 1, 1, False], + [1, 726, 7392, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 7392, 7392, 12, 12, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 1024, 782, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 912, 912, 7, 7, 1, 1, 1, 1, 0, 0, 1, 1, 1, False], + [1, 1280, 960, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 1280, 1280, 16, 16, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 1280, 1280, 16, 16, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 1280, 1280, 16, 16, 3, 3, 2, 2, 1, 1, 1, 1, 1, True], + [1, 1280, 1280, 32, 32, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 640, 1280, 32, 32, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 640, 1280, 32, 32, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 1280, 1280, 8, 8, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 1280, 1280, 8, 8, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 1280, 1920, 16, 16, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 1280, 1920, 16, 16, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 640, 1920, 32, 32, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 640, 1920, 32, 32, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 1280, 2560, 16, 16, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 1280, 2560, 16, 16, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 1280, 2560, 8, 8, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 1280, 2560, 8, 8, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 640, 320, 32, 32, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 640, 320, 32, 32, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 320, 320, 64, 64, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 320, 320, 64, 64, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 320, 320, 64, 64, 3, 3, 2, 2, 1, 1, 1, 1, 1, True], + [1, 4, 320, 64, 64, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 320, 4, 64, 64, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 1280, 640, 16, 16, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 1280, 640, 16, 16, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 640, 640, 32, 32, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 640, 640, 32, 32, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 640, 640, 32, 32, 3, 3, 2, 2, 1, 1, 1, 1, 1, True], + [1, 320, 640, 64, 64, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 320, 640, 64, 64, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 640, 640, 64, 64, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 640, 960, 32, 32, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 640, 960, 32, 32, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], + [1, 320, 960, 64, 64, 1, 1, 1, 1, 0, 0, 1, 1, 1, True], + [1, 320, 960, 64, 64, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], ], "is_conv1d": [False], }, "short_sweep_suite_conv1d": { "input_specs": [ # Contains following params - # [batch_size, output_channels, input_channels, input_length, kernel_size, stride, pad, groups, bias, dilation] - [1, 256, 1024, 512, 1, 1, 0, 1, True, 1], - [1, 1024, 256, 512, 1, 1, 0, 1, True, 1], - [1, 768, 768, 3000, 3, 2, 1, 1, True, 1], - [1, 768, 80, 3000, 3, 1, 1, 1, True, 1], + # [batch_size, output_channels, input_channels, input_length, kernel_size, stride, pad, groups, dilation, bias] + [1, 256, 1024, 512, 1, 1, 0, 1, 1, True], + [1, 1024, 256, 512, 1, 1, 0, 1, 1, True], + [1, 768, 768, 3000, 3, 2, 1, 1, 1, True], + [1, 768, 80, 3000, 3, 1, 1, 1, 1, True], ], "is_conv1d": [True], }, @@ -1615,23 +1615,23 @@ def test_conv2d_localrun(device, input_spec): failing_parameters = [ - # [batch_size, output_channels, input_channels, input_height, input_width, kernel_height, kernel_width, stride_x, stride_y, pad_x, pad_y, groups, bias, dilation] - [1, 960, 960, 27, 27, 5, 5, 2, 2, 0, 0, 960, False, 1], # 0 - [1, 960, 960, 24, 24, 5, 5, 1, 1, 2, 2, 960, False, 1], # 5 - [1, 816, 816, 19, 19, 5, 5, 1, 1, 2, 2, 816, False, 1], # 19 - [1, 816, 816, 23, 23, 5, 5, 2, 2, 0, 0, 816, False, 1], # 20 - [1, 528, 528, 192, 192, 3, 3, 2, 2, 1, 1, 2, False, 1], # 220 - [1, 819, 256, 100, 136, 3, 3, 1, 1, 1, 1, 1, True, 1], # 1443 - [1, 819, 256, 50, 68, 3, 3, 1, 1, 1, 1, 1, True, 1], # 1447 - [1, 1024, 3, 224, 224, 32, 32, 32, 32, 0, 0, 1, True, 1], # 1458 - [1, 768, 3, 224, 224, 32, 32, 32, 32, 0, 0, 1, False, 1], # 1460 - [1, 768, 3, 224, 224, 32, 32, 32, 32, 0, 0, 1, True, 1], # 1461 - [1, 768, 3, 384, 512, 32, 32, 32, 32, 0, 0, 1, True, 1], # 1464 - [1, 1, 64, 480, 640, 3, 3, 1, 1, 1, 1, 1, True, 1], # 1495 - [1, 64, 64, 480, 640, 3, 3, 1, 1, 1, 1, 1, True, 1], # 1496 - [1, 320, 320, 64, 64, 3, 3, 1, 1, 1, 1, 1, True, 1], # 1530 - [1, 320, 640, 64, 64, 3, 3, 1, 1, 1, 1, 1, True, 1], # 1540 - [1, 320, 960, 64, 64, 3, 3, 1, 1, 1, 1, 1, True, 1], # 1545 + # [batch_size, output_channels, input_channels, input_height, input_width, kernel_height, kernel_width, stride_h, stride_w, pad_h, pad_w, groups, dilation_h, dilation_w, bias] + [1, 960, 960, 27, 27, 5, 5, 2, 2, 0, 0, 960, 1, 1, False], # 0 + [1, 960, 960, 24, 24, 5, 5, 1, 1, 2, 2, 960, 1, 1, False], # 5 + [1, 816, 816, 19, 19, 5, 5, 1, 1, 2, 2, 816, 1, 1, False], # 19 + [1, 816, 816, 23, 23, 5, 5, 2, 2, 0, 0, 816, 1, 1, False], # 20 + [1, 528, 528, 192, 192, 3, 3, 2, 2, 1, 1, 2, 1, 1, False], # 220 + [1, 819, 256, 100, 136, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], # 1443 + [1, 819, 256, 50, 68, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], # 1447 + [1, 1024, 3, 224, 224, 32, 32, 32, 32, 0, 0, 1, 1, 1, True], # 1458 + [1, 768, 3, 224, 224, 32, 32, 32, 32, 0, 0, 1, 1, 1, False], # 1460 + [1, 768, 3, 224, 224, 32, 32, 32, 32, 0, 0, 1, 1, 1, True], # 1461 + [1, 768, 3, 384, 512, 32, 32, 32, 32, 0, 0, 1, 1, 1, True], # 1464 + [1, 1, 64, 480, 640, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], # 1495 + [1, 64, 64, 480, 640, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], # 1496 + [1, 320, 320, 64, 64, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], # 1530 + [1, 320, 640, 64, 64, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], # 1540 + [1, 320, 960, 64, 64, 3, 3, 1, 1, 1, 1, 1, 1, 1, True], # 1545 ] diff --git a/tests/sweep_framework/sweeps/conv2d/short/conv2d_ttforge_sweep.py b/tests/sweep_framework/sweeps/conv2d/short/conv2d_ttforge_sweep.py new file mode 100644 index 00000000000..9a30494f6bb --- /dev/null +++ b/tests/sweep_framework/sweeps/conv2d/short/conv2d_ttforge_sweep.py @@ -0,0 +1,7233 @@ +# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc. + +# SPDX-License-Identifier: Apache-2.0 + +from typing import Optional, Tuple, List +import os +import itertools +import random +import torch + +import ttnn + +from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time +from models.utility_functions import torch_random +from tests.sweep_framework.sweep_utils.conv2d_common import ( + run_conv2d_short_sweep, + mesh_device_fixture, +) + +parameters = { + "ttforge_sweep_conv2d": { + "input_specs": [ + # Contains following params + # [batch_size, output_channels, input_channels, input_height, input_width, kernel_height, kernel_width, stride_h, stride_w, pad_h, pad_w, groups, dilation_h, dilation_w, bias, [input_layout, input_memory_config, input_datatype], [weight_layout, weight_memory_config, weight_datatype], [output_layout, output_memory_config, output_datatype]] + [ + 1, + 1024, + 1024, + 14, + 14, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 2048, + 1024, + 14, + 14, + 1, + 1, + 2, + 2, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 256, + 1024, + 14, + 14, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 512, + 1024, + 14, + 14, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 1024, + 1024, + 16, + 16, + 3, + 3, + 1, + 1, + 1, + 1, + 1024, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 255, + 1024, + 16, + 16, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 512, + 1024, + 16, + 16, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 512, + 1024, + 28, + 28, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 2048, + 1024, + 45, + 80, + 1, + 1, + 2, + 2, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 256, + 1024, + 45, + 80, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 512, + 1024, + 45, + 80, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 40, + 102, + 56, + 56, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 462, + 1072, + 7, + 7, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 672, + 112, + 20, + 20, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.float32)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + ], + [ + 1, + 40, + 116, + 14, + 14, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 34, + 118, + 28, + 28, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 32, + 120, + 1, + 1, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.float32)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + ], + [ + 1, + 480, + 120, + 1, + 1, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.float32)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + ], + [ + 1, + 120, + 120, + 40, + 40, + 5, + 5, + 1, + 1, + 2, + 2, + 120, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.float32)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + ], + [ + 1, + 40, + 120, + 40, + 40, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.float32)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + ], + [ + 1, + 46, + 122, + 28, + 28, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 128, + 124, + 56, + 56, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 1280, + 1280, + 16, + 16, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 1280, + 1280, + 16, + 16, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 1280, + 1280, + 16, + 16, + 3, + 3, + 2, + 2, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 1280, + 1280, + 30, + 40, + 3, + 3, + 1, + 1, + 1, + 1, + 1280, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 1280, + 1280, + 32, + 32, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 640, + 1280, + 32, + 32, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 640, + 1280, + 32, + 32, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 1280, + 1280, + 8, + 8, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 1280, + 1280, + 8, + 8, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 128, + 128, + 112, + 112, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 64, + 128, + 120, + 160, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 128, + 128, + 128, + 128, + 3, + 3, + 1, + 1, + 1, + 1, + 128, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 256, + 128, + 128, + 128, + 3, + 3, + 2, + 2, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 64, + 128, + 128, + 128, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 64, + 128, + 128, + 128, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 128, + 128, + 180, + 320, + 3, + 3, + 2, + 2, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 128, + 128, + 1, + 1, + 3, + 3, + 1, + 1, + 1, + 1, + 128, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.float32)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + ], + [ + 1, + 24, + 128, + 1, + 1, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.float32)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + ], + [ + 1, + 546, + 128, + 1, + 1, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.float32)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.float32)], + ], + [ + 1, + 64, + 128, + 224, + 224, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 128, + 128, + 28, + 28, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 128, + 128, + 28, + 28, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 128, + 128, + 28, + 28, + 3, + 3, + 1, + 1, + 2, + 2, + 1, + 2, + 2, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 128, + 128, + 28, + 28, + 3, + 3, + 1, + 1, + 1, + 1, + 128, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 16, + 128, + 28, + 28, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 19, + 128, + 28, + 28, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), 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[int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 96, + 96, + 112, + 112, + 3, + 3, + 2, + 2, + 1, + 1, + 96, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 576, + 96, + 14, + 14, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 24, + 96, + 56, + 56, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + [ + 1, + 20, + 98, + 28, + 28, + 3, + 3, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + False, + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + [int(ttnn.ROW_MAJOR_LAYOUT), "system_memory", int(ttnn.bfloat16)], + [int(ttnn.TILE_LAYOUT), "dram", int(ttnn.bfloat16)], + ], + ], + "is_conv1d": [False], + }, +} + + +def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]: + return False, None + + +def run( + input_specs, + is_conv1d=False, + *, + device, +) -> list: + return run_conv2d_short_sweep(input_specs, device) + + +import pytest + + +@pytest.mark.parametrize("input_spec", parameters["ttforge_sweep_conv2d"]["input_specs"]) +@pytest.mark.parametrize("device_params", [{"l1_small_size": 16384}], indirect=True) +def test_conv2d_localrun(device, input_spec): + run_conv2d_short_sweep( + input_spec, + device, + ) diff --git a/tests/ttnn/unit_tests/operations/test_upsample.py b/tests/ttnn/unit_tests/operations/test_upsample.py index e4a8846e3fc..4cbe29ec4d9 100644 --- a/tests/ttnn/unit_tests/operations/test_upsample.py +++ b/tests/ttnn/unit_tests/operations/test_upsample.py @@ -10,7 +10,7 @@ import torch import torch.nn as nn import ttnn -from models.utility_functions import skip_for_grayskull, skip_for_blackhole +from models.utility_functions import skip_for_grayskull, skip_for_blackhole, is_grayskull from tests.ttnn.utils_for_testing import assert_with_pcc, check_with_pcc_without_tensor_printout @@ -109,12 +109,25 @@ def test_upsample_single_core(device, input_shapes, scale_h, scale_w): [1, 64, 132, 10], [1, 32, 8, 8], [2, 640, 32, 32], + # some random shapes + [1, 32, 5, 4], + [3, 32, 4, 4], + [5, 64, 5, 5], + [1, 128, 5, 8], + [1, 32, 5, 4], + [1, 64, 128, 17], + [1, 64, 132, 19], ], ) -@pytest.mark.parametrize("scale_h", [2]) -@pytest.mark.parametrize("scale_w", [2]) +@pytest.mark.parametrize("device_params", [{"l1_small_size": 24576}], indirect=True) +@pytest.mark.parametrize("scale_h", [2, 3]) +@pytest.mark.parametrize("scale_w", [2, 3]) @pytest.mark.parametrize("shard_strategy", [ttnn.ShardStrategy.HEIGHT, ttnn.ShardStrategy.BLOCK]) -def test_upsample_multi_core(device, input_shape, scale_h, scale_w, shard_strategy): +@pytest.mark.parametrize("shard_orientation", [ttnn.ShardOrientation.ROW_MAJOR, ttnn.ShardOrientation.COL_MAJOR]) +def test_upsample_multi_core(device, input_shape, scale_h, scale_w, shard_strategy, shard_orientation): + if is_grayskull() and (scale_h > 2 or scale_w > 2): + pytest.skip("Skipping test because it won't fit in L1!") + ## input shape is N C H W batch_size, num_channels, height, width = input_shape torch.manual_seed(0) @@ -136,15 +149,15 @@ def test_upsample_multi_core(device, input_shape, scale_h, scale_w, shard_strate max_grid_size = (device_grid.y, device_grid.x) if shard_strategy == ttnn.ShardStrategy.HEIGHT: ## nsticks per shard should be divisible by in_w - max_nshards = min(batch_size * height, max_grid_size[0] * max_grid_size[1]) + max_nshards = min(batch_size * height * width, max_grid_size[0] * max_grid_size[1]) nshards = max_nshards while nshards > 0: - if batch_size * height % nshards == 0: + if batch_size * height * width % nshards == 0: break nshards -= 1 ncores = nshards elif shard_strategy == ttnn.ShardStrategy.BLOCK: - max_nshards_h = min(batch_size * height, max_grid_size[0]) ## height along NHW + max_nshards_h = min(batch_size * height * width, max_grid_size[0]) ## height along NHW max_nshards_w = min(num_channels, max_grid_size[1]) ## width along C ## find nshards_h along NHW nshards_h = max_nshards_h @@ -177,7 +190,6 @@ def test_upsample_multi_core(device, input_shape, scale_h, scale_w, shard_strate # ) shard_grid = get_shard_grid_from_num_cores(device, ncores) - shard_orientation = ttnn.ShardOrientation.ROW_MAJOR if shard_strategy == ttnn.ShardStrategy.BLOCK: tensor_memory_layout = ttnn.types.TensorMemoryLayout.BLOCK_SHARDED @@ -351,6 +363,7 @@ def test_bilinear_multi_core( ## compare the results torch_result = torch_result.permute(0, 2, 3, 1) + passing, pcc_msg = check_with_pcc_without_tensor_printout(torch_result, output_tensor, pcc=0.999) allclose = torch.allclose(output_tensor, torch_result, atol=1e-1, rtol=1e-1) logger.info(pcc_msg) diff --git a/tt_metal/impl/debug/watcher_device_reader.cpp b/tt_metal/impl/debug/watcher_device_reader.cpp index 1660cebc11a..dc23aa1628b 100644 --- a/tt_metal/impl/debug/watcher_device_reader.cpp +++ b/tt_metal/impl/debug/watcher_device_reader.cpp @@ -24,16 +24,26 @@ #include "hw/inc/debug/ring_buffer.h" #include "impl/device/device.hpp" #include "llrt/rtoptions.hpp" -#include "noc/noc_overlay_parameters.h" -#include "noc/noc_parameters.h" #include "watcher_device_reader.hpp" #include "llrt/hal.hpp" using namespace tt::tt_metal; - using std::string; + +#define NOC_MCAST_ADDR_START_X(addr) (hal.get_noc_mcast_addr_start_x(addr)) +#define NOC_MCAST_ADDR_START_Y(addr) (hal.get_noc_mcast_addr_start_y(addr)) +#define NOC_MCAST_ADDR_END_X(addr) (hal.get_noc_mcast_addr_end_x(addr)) +#define NOC_MCAST_ADDR_END_Y(addr) (hal.get_noc_mcast_addr_end_y(addr)) +#define NOC_UNICAST_ADDR_X(addr) (hal.get_noc_ucast_addr_x(addr)) +#define NOC_UNICAST_ADDR_Y(addr) (hal.get_noc_ucast_addr_y(addr)) +#define NOC_LOCAL_ADDR(addr) (hal.get_noc_local_addr(addr)) +#define NOC_OVERLAY_START_ADDR (hal.get_noc_overlay_start_addr()) +#define NOC_STREAM_REG_SPACE_SIZE (hal.get_noc_stream_reg_space_size()) +#define STREAM_REMOTE_DEST_BUF_SIZE_REG_INDEX (hal.get_noc_stream_remote_dest_buf_size_reg_index()) +#define STREAM_REMOTE_DEST_BUF_START_REG_INDEX (hal.get_noc_stream_remote_dest_buf_start_reg_index()) + namespace { // Helper functions // Helper function to get string rep of riscv type diff --git a/tt_metal/impl/device/device.cpp b/tt_metal/impl/device/device.cpp index b00adee7e9e..03228ecb436 100644 --- a/tt_metal/impl/device/device.cpp +++ b/tt_metal/impl/device/device.cpp @@ -28,9 +28,6 @@ #include "tt_metal/impl/sub_device/sub_device_types.hpp" #include "tt_metal/tt_stl/span.hpp" #include "tt_metal/types.hpp" -#include "noc/noc_parameters.h" - -// FIXME: ARCH_NAME specific #include "impl/dispatch/topology.hpp" namespace tt { @@ -1717,7 +1714,8 @@ void Device::generate_device_bank_to_noc_tables() for (unsigned int bank_id = 0; bank_id < dram_noc_coord_per_bank.size(); bank_id++) { uint16_t noc_x = tt::tt_metal::hal.noc_coordinate(noc, soc_d.grid_size.x, dram_noc_coord_per_bank[bank_id].x); uint16_t noc_y = tt::tt_metal::hal.noc_coordinate(noc, soc_d.grid_size.y, dram_noc_coord_per_bank[bank_id].y); - uint16_t xy = ((noc_y << NOC_ADDR_NODE_ID_BITS) | noc_x) << NOC_COORD_REG_OFFSET; + uint16_t xy = ((noc_y << tt::tt_metal::hal.get_noc_addr_node_id_bits()) | noc_x) + << tt::tt_metal::hal.get_noc_coord_reg_offset(); dram_bank_to_noc_xy_.push_back(xy); } } @@ -1729,7 +1727,8 @@ void Device::generate_device_bank_to_noc_tables() auto l1_noc_coords = this->virtual_noc0_coordinate(noc, l1_noc_coord_per_bank[bank_id]); uint16_t noc_x = l1_noc_coords.x; uint16_t noc_y = l1_noc_coords.y; - uint16_t xy = ((noc_y << NOC_ADDR_NODE_ID_BITS) | noc_x) << NOC_COORD_REG_OFFSET; + uint16_t xy = ((noc_y << tt::tt_metal::hal.get_noc_addr_node_id_bits()) | noc_x) + << tt::tt_metal::hal.get_noc_coord_reg_offset(); l1_bank_to_noc_xy_.push_back(xy); } } diff --git a/tt_metal/llrt/blackhole/bh_hal.cpp b/tt_metal/llrt/blackhole/bh_hal.cpp index ec46fa6efc6..39a955660ec 100644 --- a/tt_metal/llrt/blackhole/bh_hal.cpp +++ b/tt_metal/llrt/blackhole/bh_hal.cpp @@ -77,6 +77,13 @@ void Hal::initialize_bh() { this->noc_multicast_encoding_func_ = [](uint32_t x_start, uint32_t y_start, uint32_t x_end, uint32_t y_end) { return NOC_MULTICAST_ENCODING(x_start, y_start, x_end, y_end); }; + this->noc_mcast_addr_start_x_func_ = [](uint64_t addr) -> uint64_t { return NOC_MCAST_ADDR_START_X(addr); }; + this->noc_mcast_addr_start_y_func_ = [](uint64_t addr) -> uint64_t { return NOC_MCAST_ADDR_START_Y(addr); }; + this->noc_mcast_addr_end_x_func_ = [](uint64_t addr) -> uint64_t { return NOC_MCAST_ADDR_END_X(addr); }; + this->noc_mcast_addr_end_y_func_ = [](uint64_t addr) -> uint64_t { return NOC_MCAST_ADDR_END_Y(addr); }; + this->noc_ucast_addr_x_func_ = [](uint64_t addr) -> uint64_t { return NOC_UNICAST_ADDR_X(addr); }; + this->noc_ucast_addr_y_func_ = [](uint64_t addr) -> uint64_t { return NOC_UNICAST_ADDR_Y(addr); }; + this->noc_local_addr_func_ = [](uint64_t addr) -> uint64_t { return NOC_LOCAL_ADDR(addr); }; this->stack_size_func_ = [](uint32_t type) -> uint32_t { switch (type) { @@ -93,6 +100,12 @@ void Hal::initialize_bh() { }; this->num_nocs_ = NUM_NOCS; + this->noc_addr_node_id_bits_ = NOC_ADDR_NODE_ID_BITS; + this->noc_coord_reg_offset_ = NOC_COORD_REG_OFFSET; + this->noc_overlay_start_addr_ = NOC_OVERLAY_START_ADDR; + this->noc_stream_reg_space_size_ = NOC_STREAM_REG_SPACE_SIZE; + this->noc_stream_remote_dest_buf_size_reg_index_ = STREAM_REMOTE_DEST_BUF_SIZE_REG_INDEX; + this->noc_stream_remote_dest_buf_start_reg_index_ = STREAM_REMOTE_DEST_BUF_START_REG_INDEX; this->coordinate_virtualization_enabled_ = COORDINATE_VIRTUALIZATION_ENABLED; this->virtual_worker_start_x_ = VIRTUAL_TENSIX_START_X; this->virtual_worker_start_y_ = VIRTUAL_TENSIX_START_Y; diff --git a/tt_metal/llrt/grayskull/gs_hal.cpp b/tt_metal/llrt/grayskull/gs_hal.cpp index 7a2d51791cb..da549bfaa6d 100644 --- a/tt_metal/llrt/grayskull/gs_hal.cpp +++ b/tt_metal/llrt/grayskull/gs_hal.cpp @@ -160,6 +160,13 @@ void Hal::initialize_gs() { this->noc_multicast_encoding_func_ = [](uint32_t x_start, uint32_t y_start, uint32_t x_end, uint32_t y_end) { return NOC_MULTICAST_ENCODING(x_start, y_start, x_end, y_end); }; + this->noc_mcast_addr_start_x_func_ = [](uint64_t addr) -> uint64_t { return NOC_MCAST_ADDR_START_X(addr); }; + this->noc_mcast_addr_start_y_func_ = [](uint64_t addr) -> uint64_t { return NOC_MCAST_ADDR_START_Y(addr); }; + this->noc_mcast_addr_end_x_func_ = [](uint64_t addr) -> uint64_t { return NOC_MCAST_ADDR_END_X(addr); }; + this->noc_mcast_addr_end_y_func_ = [](uint64_t addr) -> uint64_t { return NOC_MCAST_ADDR_END_Y(addr); }; + this->noc_ucast_addr_x_func_ = [](uint64_t addr) -> uint64_t { return NOC_UNICAST_ADDR_X(addr); }; + this->noc_ucast_addr_y_func_ = [](uint64_t addr) -> uint64_t { return NOC_UNICAST_ADDR_Y(addr); }; + this->noc_local_addr_func_ = [](uint64_t addr) -> uint64_t { return NOC_LOCAL_ADDR(addr); }; this->stack_size_func_ = [](uint32_t type) -> uint32_t { switch (type) { @@ -176,6 +183,12 @@ void Hal::initialize_gs() { }; this->num_nocs_ = NUM_NOCS; + this->noc_addr_node_id_bits_ = NOC_ADDR_NODE_ID_BITS; + this->noc_coord_reg_offset_ = NOC_COORD_REG_OFFSET; + this->noc_overlay_start_addr_ = NOC_OVERLAY_START_ADDR; + this->noc_stream_reg_space_size_ = NOC_STREAM_REG_SPACE_SIZE; + this->noc_stream_remote_dest_buf_size_reg_index_ = STREAM_REMOTE_DEST_BUF_SIZE_REG_INDEX; + this->noc_stream_remote_dest_buf_start_reg_index_ = STREAM_REMOTE_DEST_BUF_START_REG_INDEX; this->coordinate_virtualization_enabled_ = COORDINATE_VIRTUALIZATION_ENABLED; this->virtual_worker_start_x_ = VIRTUAL_TENSIX_START_X; this->virtual_worker_start_y_ = VIRTUAL_TENSIX_START_Y; diff --git a/tt_metal/llrt/hal.hpp b/tt_metal/llrt/hal.hpp index ea06aad8fb4..1d602f4dd05 100644 --- a/tt_metal/llrt/hal.hpp +++ b/tt_metal/llrt/hal.hpp @@ -144,6 +144,7 @@ class Hal { using ValidRegAddrFunc = std::function; using NOCXYEncodingFunc = std::function; using NOCMulticastEncodingFunc = std::function; + using NOCAddrFunc = std::function; using StackSizeFunc = std::function; private: @@ -153,6 +154,12 @@ class Hal { std::vector dram_sizes_; std::vector mem_alignments_; uint32_t num_nocs_; + uint32_t noc_addr_node_id_bits_; + uint32_t noc_coord_reg_offset_; + uint32_t noc_overlay_start_addr_; + uint32_t noc_stream_reg_space_size_; + uint32_t noc_stream_remote_dest_buf_size_reg_index_; + uint32_t noc_stream_remote_dest_buf_start_reg_index_; bool coordinate_virtualization_enabled_; uint32_t virtual_worker_start_x_; uint32_t virtual_worker_start_y_; @@ -166,6 +173,13 @@ class Hal { ValidRegAddrFunc valid_reg_addr_func_; NOCXYEncodingFunc noc_xy_encoding_func_; NOCMulticastEncodingFunc noc_multicast_encoding_func_; + NOCAddrFunc noc_mcast_addr_start_x_func_; + NOCAddrFunc noc_mcast_addr_start_y_func_; + NOCAddrFunc noc_mcast_addr_end_x_func_; + NOCAddrFunc noc_mcast_addr_end_y_func_; + NOCAddrFunc noc_ucast_addr_x_func_; + NOCAddrFunc noc_ucast_addr_y_func_; + NOCAddrFunc noc_local_addr_func_; StackSizeFunc stack_size_func_; public: @@ -174,6 +188,17 @@ class Hal { tt::ARCH get_arch() const { return arch_; } uint32_t get_num_nocs() const { return num_nocs_; } + uint32_t get_noc_addr_node_id_bits() const { return noc_addr_node_id_bits_; } + uint32_t get_noc_coord_reg_offset() const { return noc_coord_reg_offset_; } + + uint32_t get_noc_overlay_start_addr() const { return noc_overlay_start_addr_; } + uint32_t get_noc_stream_reg_space_size() const { return noc_stream_reg_space_size_; } + uint32_t get_noc_stream_remote_dest_buf_size_reg_index() const { + return noc_stream_remote_dest_buf_size_reg_index_; + } + uint32_t get_noc_stream_remote_dest_buf_start_reg_index() const { + return noc_stream_remote_dest_buf_start_reg_index_; + } template auto noc_coordinate(IndexType noc_index, SizeType noc_size, CoordType coord) const @@ -186,6 +211,14 @@ class Hal { return noc_multicast_encoding_func_(x_start, y_start, x_end, y_end); } + uint64_t get_noc_mcast_addr_start_x(uint64_t addr) const { return noc_mcast_addr_start_x_func_(addr); } + uint64_t get_noc_mcast_addr_start_y(uint64_t addr) const { return noc_mcast_addr_start_y_func_(addr); } + uint64_t get_noc_mcast_addr_end_x(uint64_t addr) const { return noc_mcast_addr_end_x_func_(addr); } + uint64_t get_noc_mcast_addr_end_y(uint64_t addr) const { return noc_mcast_addr_end_y_func_(addr); } + uint64_t get_noc_ucast_addr_x(uint64_t addr) const { return noc_ucast_addr_x_func_(addr); } + uint64_t get_noc_ucast_addr_y(uint64_t addr) const { return noc_ucast_addr_y_func_(addr); } + uint64_t get_noc_local_addr(uint64_t addr) const { return noc_local_addr_func_(addr); } + bool is_coordinate_virtualization_enabled() const { return this->coordinate_virtualization_enabled_; }; std::uint32_t get_virtual_worker_start_x() const { return this->virtual_worker_start_x_; } std::uint32_t get_virtual_worker_start_y() const { return this->virtual_worker_start_y_; } diff --git a/tt_metal/llrt/wormhole/wh_hal.cpp b/tt_metal/llrt/wormhole/wh_hal.cpp index 29e4c619339..d730c4442dd 100644 --- a/tt_metal/llrt/wormhole/wh_hal.cpp +++ b/tt_metal/llrt/wormhole/wh_hal.cpp @@ -78,6 +78,13 @@ void Hal::initialize_wh() { this->noc_multicast_encoding_func_ = [](uint32_t x_start, uint32_t y_start, uint32_t x_end, uint32_t y_end) { return NOC_MULTICAST_ENCODING(x_start, y_start, x_end, y_end); }; + this->noc_mcast_addr_start_x_func_ = [](uint64_t addr) -> uint64_t { return NOC_MCAST_ADDR_START_X(addr); }; + this->noc_mcast_addr_start_y_func_ = [](uint64_t addr) -> uint64_t { return NOC_MCAST_ADDR_START_Y(addr); }; + this->noc_mcast_addr_end_x_func_ = [](uint64_t addr) -> uint64_t { return NOC_MCAST_ADDR_END_X(addr); }; + this->noc_mcast_addr_end_y_func_ = [](uint64_t addr) -> uint64_t { return NOC_MCAST_ADDR_END_Y(addr); }; + this->noc_ucast_addr_x_func_ = [](uint64_t addr) -> uint64_t { return NOC_UNICAST_ADDR_X(addr); }; + this->noc_ucast_addr_y_func_ = [](uint64_t addr) -> uint64_t { return NOC_UNICAST_ADDR_Y(addr); }; + this->noc_local_addr_func_ = [](uint64_t addr) -> uint64_t { return NOC_LOCAL_ADDR(addr); }; this->stack_size_func_ = [](uint32_t type) -> uint32_t { switch (type) { @@ -94,6 +101,12 @@ void Hal::initialize_wh() { }; this->num_nocs_ = NUM_NOCS; + this->noc_addr_node_id_bits_ = NOC_ADDR_NODE_ID_BITS; + this->noc_coord_reg_offset_ = NOC_COORD_REG_OFFSET; + this->noc_overlay_start_addr_ = NOC_OVERLAY_START_ADDR; + this->noc_stream_reg_space_size_ = NOC_STREAM_REG_SPACE_SIZE; + this->noc_stream_remote_dest_buf_size_reg_index_ = STREAM_REMOTE_DEST_BUF_SIZE_REG_INDEX; + this->noc_stream_remote_dest_buf_start_reg_index_ = STREAM_REMOTE_DEST_BUF_START_REG_INDEX; this->coordinate_virtualization_enabled_ = COORDINATE_VIRTUALIZATION_ENABLED; this->virtual_worker_start_x_ = VIRTUAL_TENSIX_START_X; this->virtual_worker_start_y_ = VIRTUAL_TENSIX_START_Y; diff --git a/ttnn/cpp/ttnn/operations/pool/upsample/device/kernels/dataflow/writer_upsample_multi_core_sharded.cpp b/ttnn/cpp/ttnn/operations/pool/upsample/device/kernels/dataflow/writer_upsample_multi_core_sharded.cpp index 001da7bba6e..44876387e1c 100644 --- a/ttnn/cpp/ttnn/operations/pool/upsample/device/kernels/dataflow/writer_upsample_multi_core_sharded.cpp +++ b/ttnn/cpp/ttnn/operations/pool/upsample/device/kernels/dataflow/writer_upsample_multi_core_sharded.cpp @@ -7,59 +7,48 @@ void kernel_main() { uint32_t stick_nbytes = get_arg_val(0); - uint32_t in_image_rows_per_core = get_arg_val(1); + uint32_t in_nsticks_per_core = get_arg_val(1); uint32_t scale_h = get_arg_val(2); uint32_t scale_w = get_arg_val(3); - uint32_t in_w = get_arg_val(4); - uint32_t out_w = get_arg_val(5); constexpr uint32_t in_cb_id = get_compile_time_arg_val(0); constexpr uint32_t out_cb_id = get_compile_time_arg_val(1); constexpr uint32_t is_reader = get_compile_time_arg_val(2); - - uint32_t in_image_row_nbytes = in_w * stick_nbytes; - uint32_t out_image_row_nbytes = out_w * stick_nbytes; - uint32_t reader_image_rows_per_core = (in_image_rows_per_core + is_reader) / 2; - uint32_t writer_image_rows_per_core = in_image_rows_per_core / 2; - uint32_t image_row_begin = is_reader ? 0 : reader_image_rows_per_core; - uint32_t image_row_end = is_reader ? reader_image_rows_per_core : in_image_rows_per_core; - uint32_t l1_read_addr = get_read_ptr(in_cb_id) + image_row_begin * in_image_row_nbytes; - uint32_t l1_write_addr = get_write_ptr(out_cb_id) + image_row_begin * scale_h * out_image_row_nbytes; - - cb_reserve_back(out_cb_id, out_w); - - // assuming shard begins with a new row. TODO: generalize? - for (uint32_t image_row = image_row_begin; image_row < image_row_end; ++image_row) { - uint32_t l1_write_addr_image_row_start = l1_write_addr; - for (uint32_t i = 0; i < in_w; ++i) { + constexpr uint32_t config_cb_id = get_compile_time_arg_val(3); + + uint32_t reader_nsticks_per_core = (in_nsticks_per_core + is_reader) / 2; + uint32_t out_nsticks_per_core = reader_nsticks_per_core * scale_h * scale_w; + uint32_t image_row_begin = is_reader ? 0 : reader_nsticks_per_core; + uint32_t image_row_end = is_reader ? reader_nsticks_per_core : in_nsticks_per_core; + uint32_t l1_read_addr = get_read_ptr(in_cb_id); + uint32_t l1_write_addr = get_write_ptr(out_cb_id) + image_row_begin * scale_h * scale_w * stick_nbytes; + + uint32_t config_l1_addr = get_read_ptr(config_cb_id); + volatile tt_l1_ptr uint16_t* config_data = reinterpret_cast(config_l1_addr); + + uint32_t reader_idx = 0; + if constexpr (!is_reader) { + /* For each input stick there are 2 entries in config cb {{core_coords.x, core_coords.y}, stick_offset(in + * input_cb)} so multiply input image_row_begin with (2 * scale_h) */ + reader_idx = (2 * scale_h) * image_row_begin; + } + cb_reserve_back(out_cb_id, out_nsticks_per_core); + + for (uint32_t row_begin = image_row_begin; row_begin < image_row_end; ++row_begin) { + for (uint32_t sh = 0; sh < scale_h; sh++) { + uint16_t cores = config_data[reader_idx++]; + uint16_t corey = cores & 0xFF; + uint16_t corex = cores >> 8; + uint16_t offset = config_data[reader_idx++]; + uint64_t src_remote_addr = get_noc_addr(corex, corey, l1_read_addr + offset * stick_nbytes); // replicate stick scale_w times. - for (uint32_t sw = 0; sw < scale_w; ++sw) { - // replicate stick scale_w times. - if constexpr (is_reader) { - uint64_t src_noc_addr = get_noc_addr(l1_read_addr); - noc_async_read(src_noc_addr, l1_write_addr, stick_nbytes); - } else { - uint64_t dst_noc_addr = get_noc_addr(l1_write_addr); - noc_async_write(l1_read_addr, dst_noc_addr, stick_nbytes); - } + for (uint32_t sw = 0; sw < scale_w; sw++) { + noc_async_read(src_remote_addr, l1_write_addr, stick_nbytes); l1_write_addr += stick_nbytes; } - l1_read_addr += stick_nbytes; } - - // Duplicate the whole image row in one shot - if constexpr (is_reader) { - uint64_t src_noc_addr = get_noc_addr(l1_write_addr_image_row_start); - noc_async_read(src_noc_addr, l1_write_addr, out_image_row_nbytes); - } else { - uint64_t dst_noc_addr = get_noc_addr(l1_write_addr); - noc_async_write(l1_write_addr_image_row_start, dst_noc_addr, out_image_row_nbytes); - } - l1_write_addr += out_image_row_nbytes; } - cb_push_back(out_cb_id, out_w); - - noc_async_write_barrier(); noc_async_read_barrier(); + cb_push_back(out_cb_id, out_nsticks_per_core); } diff --git a/ttnn/cpp/ttnn/operations/pool/upsample/device/upsample_program_factory_multicore.cpp b/ttnn/cpp/ttnn/operations/pool/upsample/device/upsample_program_factory_multicore.cpp index 79eab51d7cf..a6f4de9c881 100644 --- a/ttnn/cpp/ttnn/operations/pool/upsample/device/upsample_program_factory_multicore.cpp +++ b/ttnn/cpp/ttnn/operations/pool/upsample/device/upsample_program_factory_multicore.cpp @@ -2,6 +2,8 @@ // // SPDX-License-Identifier: Apache-2.0 +#include + #include #include "upsample_op.hpp" @@ -13,6 +15,7 @@ #include "tt_metal/common/math.hpp" #include "tt_metal/tt_stl/reflection.hpp" +#include "ttnn/tensor/host_buffer/functions.hpp" using namespace tt::constants; using namespace tt::tt_metal; @@ -20,6 +23,106 @@ using namespace tt::tt_metal; namespace ttnn::operations::upsample { using namespace tt; +static Tensor create_config_tensor( + IDevice* device, + ShardSpec shard_spec, + const uint32_t batch_size, + const uint32_t in_h, + const uint32_t in_w, + const uint32_t scale_factor_h, + const uint32_t scale_factor_w, + const uint32_t ncores_x, + const bool is_height_sharded, + const bool is_col_major) { + uint16_t in_core = 0, curr_stick = 0; + const uint32_t input_nsticks_per_core = shard_spec.shape[0]; + + std::vector> core_range; + auto ranges = shard_spec.grid.ranges(); + // in case of height sharding and shards arranged in column major order, get cores where shard are placed. + if (is_col_major && is_height_sharded) { + for (auto i = 0; i < ranges.size(); i++) { + auto range = ranges[i]; + for (auto x = range.start_coord.x; x <= range.end_coord.x; x++) { + for (auto y = range.start_coord.y; y <= range.end_coord.y; y++) { + core_range.push_back({x, y}); + } + } + } + } + + std::vector logical_core_to_stick_map; + size_t logical_core_to_stick_map_entry_size = 3; + size_t row_size = logical_core_to_stick_map_entry_size * in_w; + // Create map of core and respective offsets in input + for (uint32_t b = 0; b < batch_size; ++b) { + for (uint32_t h = 0; h < in_h; ++h) { + for (uint32_t w = 0; w < in_w; ++w, ++curr_stick) { + if (curr_stick == input_nsticks_per_core) { + curr_stick = 0, ++in_core; + } + if (is_height_sharded && is_col_major) { + logical_core_to_stick_map.push_back(core_range[in_core][0]); + logical_core_to_stick_map.push_back(core_range[in_core][1]); + } else { + logical_core_to_stick_map.push_back(in_core); + logical_core_to_stick_map.push_back(0); + } + logical_core_to_stick_map.push_back(curr_stick); + } + for (uint32_t j = 1; j < scale_factor_h; ++j) { + logical_core_to_stick_map.insert( + logical_core_to_stick_map.end(), + logical_core_to_stick_map.end() - row_size, + logical_core_to_stick_map.end()); + } + } + } + + std::vector config_vector; + + // Based on core calculate physical location of cores + CoreCoord core_coords; + if (is_height_sharded) { + for (size_t j = 0; j < logical_core_to_stick_map.size(); j += logical_core_to_stick_map_entry_size) { + if (is_col_major) { + core_coords = device->worker_core_from_logical_core( + CoreCoord(logical_core_to_stick_map[j], logical_core_to_stick_map[j + 1])); + } else { + core_coords = device->worker_core_from_logical_core( + CoreCoord(logical_core_to_stick_map[j] % ncores_x, logical_core_to_stick_map[j] / ncores_x)); + } + // Combine the x and y coordinates of the core into a single 16-bit value. + // The x coordinate is shifted left by 8 bits and added to the y coordinate. + uint16_t cores = (core_coords.x << 8) + core_coords.y; + config_vector.push_back(cores); + config_vector.push_back(logical_core_to_stick_map[j + 2]); + } + } else { + for (size_t i = 0; i < ncores_x; i++) { + for (size_t j = 0; j < logical_core_to_stick_map.size(); j += logical_core_to_stick_map_entry_size) { + core_coords = device->worker_core_from_logical_core(CoreCoord(i, logical_core_to_stick_map[j])); + // Combine the x and y coordinates of the core into a single 16-bit value. + // The x coordinate is shifted left by 8 bits and added to the y coordinate. + uint16_t cores = (core_coords.x << 8) + core_coords.y; + config_vector.push_back(cores); + config_vector.push_back(logical_core_to_stick_map[j + 2]); + } + } + } + /* Each entry in config_vector contains 2 elements: + * {{core_coords.x, core_coords.y}, stick_offset(in input_cb)} + * - core_coords.x: X coordinate of the core + * - core_coords.y: Y coordinate of the core + * - stick_offset: Offset within the input circular buffer + */ + const uint32_t config_buffer_entry_size = 2; + uint32_t elems_per_core = config_buffer_entry_size * scale_factor_h * input_nsticks_per_core; + Shape config_shape({config_vector.size() / elems_per_core, elems_per_core}); + auto config_buffer = owned_buffer::create(std::move(config_vector)); + return Tensor(OwnedStorage{config_buffer}, config_shape, DataType::UINT16, Layout::ROW_MAJOR); +} + operation::ProgramWithCallbacks upsample_multi_core( const Tensor& input, Tensor& output, const uint32_t scale_factor_h, const uint32_t scale_factor_w) { Program program = CreateProgram(); @@ -55,23 +158,17 @@ operation::ProgramWithCallbacks upsample_multi_core( ncores); uint32_t in_nsticks_per_core = shard_spec.shape[0]; - uint32_t out_nsticks_per_core = in_nsticks_per_core * scale_factor_h * scale_factor_w; + + if (input.memory_config().memory_layout == TensorMemoryLayout::WIDTH_SHARDED) { + TT_THROW("Unsupported sharding layout"); + } // extra limitation to avoid post upsample step of resharding - if (input.memory_config().memory_layout == TensorMemoryLayout::HEIGHT_SHARDED) { - TT_FATAL( - in_nsticks_per_core % in_w == 0, - "Restriction: Input sticks per core {} should be divisible by input width {}. TODO to remove this " - "restriction", - in_nsticks_per_core, - in_w); - } else if (input.memory_config().memory_layout == TensorMemoryLayout::BLOCK_SHARDED) { + if (input.memory_config().memory_layout == TensorMemoryLayout::BLOCK_SHARDED) { ncores_x = all_cores.ranges().begin()->end_coord.x + 1; ncores_nhw = all_cores.ranges().begin()->end_coord.y + 1; input_stick_nbytes = input_stick_nbytes / ncores_x; output_stick_nbytes = output_stick_nbytes / ncores_x; - } else { - TT_THROW("Unsupported sharding layout"); } uint32_t input_nsticks_per_core = div_up(input_nsticks, ncores_nhw); @@ -83,12 +180,6 @@ operation::ProgramWithCallbacks upsample_multi_core( "Input sticks per shard {} should be same as input sticks per core {}", in_nsticks_per_core, input_nsticks_per_core); - TT_FATAL( - out_nsticks_per_core == output_nsticks_per_core, - "Output sticks per shard {} should be same as output sticks per core {}", - out_nsticks_per_core, - output_nsticks_per_core); - TT_FATAL(input_nsticks_per_core % in_w == 0, "Error"); // CBs @@ -126,12 +217,49 @@ operation::ProgramWithCallbacks upsample_multi_core( input_nsticks_per_core, output_nsticks_per_core); + // create config tensor + Tensor config_tensor; + if ((input.memory_config().memory_layout == TensorMemoryLayout::BLOCK_SHARDED) || + (input.memory_config().memory_layout == TensorMemoryLayout::HEIGHT_SHARDED)) { + config_tensor = create_config_tensor( + device, + shard_spec, + input.legacy_shape()[0], + input.legacy_shape()[1], + in_w, + scale_factor_h, + scale_factor_w, + ncores_x, + input.memory_config().memory_layout == TensorMemoryLayout::HEIGHT_SHARDED, + shard_spec.orientation == ShardOrientation::COL_MAJOR); + } else { + TT_THROW("Unsupported sharding layout"); + } + auto shard_shape = std::array({1, (uint32_t)config_tensor.get_shape()[-1]}); + auto config_tensor_shard_orientation = input.memory_config().memory_layout == TensorMemoryLayout::BLOCK_SHARDED + ? ShardOrientation::COL_MAJOR + : shard_spec.orientation; + ShardSpec config_shard_spec(input.shard_spec().value().grid, shard_shape, config_tensor_shard_orientation, false); + MemoryConfig memory_config{input.memory_config().memory_layout, BufferType::L1_SMALL, config_shard_spec}; + auto config_tensor_device = config_tensor.to(device, memory_config); + tt::tt_metal::detail::AddConfigBuffer(program, config_tensor_device.device_buffer()); + + tt::DataFormat config_df = tt::DataFormat::RawUInt16; + Buffer* config_buffer = config_tensor_device.buffer(); + auto config_buffer_page_size = config_buffer->page_size(); + uint32_t config_cb_id = CBIndex::c_6; + auto config_cb_config = CircularBufferConfig(config_buffer_page_size, {{config_cb_id, config_df}}) + .set_page_size(config_cb_id, config_buffer->page_size()) + .set_globally_allocated_address(*config_buffer); + CBHandle config_cb = CreateCircularBuffer(program, all_cores, config_cb_config); + // Kernels std::vector writer_compile_time_args = { in_cb_id, out_cb_id, false, + config_cb_id, }; auto writer_kernel_fname = std::string( "ttnn/cpp/ttnn/operations/pool/upsample/device/kernels/dataflow/writer_upsample_multi_core_sharded.cpp"); @@ -142,6 +270,7 @@ operation::ProgramWithCallbacks upsample_multi_core( in_cb_id, out_cb_id, true, + config_cb_id, }; auto reader_kernel_fname = std::string( "ttnn/cpp/ttnn/operations/pool/upsample/device/kernels/dataflow/writer_upsample_multi_core_sharded.cpp"); @@ -155,12 +284,9 @@ operation::ProgramWithCallbacks upsample_multi_core( uint32_t writer_nargs = 7; std::vector writer_rt_args(writer_nargs); writer_rt_args[0] = input_stick_nbytes; - writer_rt_args[1] = input_nsticks_per_core / in_w; + writer_rt_args[1] = input_nsticks_per_core; writer_rt_args[2] = scale_factor_h; writer_rt_args[3] = scale_factor_w; - writer_rt_args[4] = in_w; - writer_rt_args[5] = out_w; - writer_rt_args[6] = 0; // set for each core below uint32_t start_input_stick_id = 0; if (input.memory_config().memory_layout == TensorMemoryLayout::BLOCK_SHARDED) { @@ -185,7 +311,7 @@ operation::ProgramWithCallbacks upsample_multi_core( TT_THROW("Unsupported memory layout"); } - auto override_runtime_args_callback = [writer_kernel, cb_src0, out_cb]( + auto override_runtime_args_callback = [writer_kernel, cb_src0, out_cb, config_cb]( const void* operation, Program& program, const std::vector& input_tensors,