diff --git a/.gitignore b/.gitignore index 79cb12d..78267d9 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,5 @@ */*/__pycache__/ */*/*/*/__pycache__/ */*/*/*/*/__pycache__/ +*.pyc +temp/ diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/embedding_triton_kernel.py b/reflexion_oneshot_tritonbench_iter1_4/exec/embedding_triton_kernel.py new file mode 100644 index 0000000..edd1eec --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/embedding_triton_kernel.py @@ -0,0 +1,153 @@ +import torch +import triton +import triton.language as tl + +@triton.jit +def embedding_kernel(weight, input_ids, out, vob_start_id, vob_end_id, stride_weight_row, stride_out_row, n_ctx, hiden_size, BLOCK_N: tl.constexpr, BLOCK_DMODEL: tl.constexpr): + pid = tl.program_id(0) + offs_n = pid * BLOCK_N + tl.arange(0, BLOCK_N) + offs_d = tl.arange(0, BLOCK_DMODEL) + mask_n = offs_n < n_ctx + token_ids_raw = tl.load(input_ids + offs_n, mask=mask_n, other=vob_end_id) + valid_id_mask = (token_ids_raw >= vob_start_id) & (token_ids_raw < vob_end_id) + token_ids_clamped = tl.where(valid_id_mask, token_ids_raw - vob_start_id, 0) + offs_vec = token_ids_clamped[:, None] * stride_weight_row + offs_d[None, :] + load_mask = valid_id_mask[:, None] & (offs_d[None, :] < hiden_size) + vec = tl.load(weight + offs_vec, mask=load_mask, other=0.0) + vec = tl.where(valid_id_mask[:, None], vec, 0.0) + dest_offs = offs_n[:, None] * stride_out_row + offs_d[None, :] + store_mask = mask_n[:, None] & (offs_d[None, :] < hiden_size) + tl.store(out + dest_offs, vec, mask=store_mask) + +@torch.no_grad() +def embedding(input_ids: torch.Tensor, weight: torch.Tensor, vob_start_id: int, vob_end_id: int, out: torch.Tensor): + assert input_ids.ndim == 1 + assert weight.ndim == 2 + assert out.ndim == 2 and out.shape[0] == input_ids.shape[0] and (out.shape[1] == weight.shape[1]) + n_ctx = input_ids.shape[0] + BLOCK_DMODEL = triton.next_power_of_2(weight.shape[1]) + BLOCK_N = 128 + grid = (triton.cdiv(n_ctx, BLOCK_N),) + embedding_kernel[grid](weight, input_ids, out, vob_start_id, vob_end_id, weight.stride(0), out.stride(0), n_ctx, weight.shape[1], BLOCK_N=BLOCK_N, BLOCK_DMODEL=BLOCK_DMODEL, num_warps=4, num_stages=1) + +################################################################################################################################################## + + + + + +import torch + + + +def test_embedding(): + + # 参数定义 + + vocab_size = 1000 # 词汇表大小 + + embedding_dim = 512 # 嵌入维度 + + sequence_length = 128 # 输入序列长度 + + vob_start_id = 10 # 词汇表起始 ID + + vob_end_id = 1000 # 词汇表结束 ID + + + + # 创建测试输入张量 + + input_ids = torch.randint( + + vob_start_id, vob_end_id, (sequence_length,), dtype=torch.int32, device='cuda' + + ) + + weight = torch.randn( + + vocab_size, embedding_dim, dtype=torch.float32, device='cuda' + + ) + + out = torch.zeros( + + sequence_length, embedding_dim, dtype=torch.float32, device='cuda' + + ) + + + + # 调用嵌入函数 + + embedding(input_ids, weight, vob_start_id, vob_end_id, out) + + + + # 保存结果 + + results = {} + + results['test_case_1'] = out.clone() + + + + # 测试不同的输入 + + input_ids = torch.randint( + + vob_start_id, vob_end_id, (sequence_length,), dtype=torch.int32, device='cuda' + + ) + + embedding(input_ids, weight, vob_start_id, vob_end_id, out) + + results['test_case_2'] = out.clone() + + + + # 测试不同的词汇表范围 + + vob_start_id = 0 + + vob_end_id = 500 + + input_ids = torch.randint( + + vob_start_id, vob_end_id, (sequence_length,), dtype=torch.int32, device='cuda' + + ) + + embedding(input_ids, weight, vob_start_id, vob_end_id, out) + + results['test_case_3'] = out.clone() + + + + # 测试不同的嵌入维度 + + embedding_dim = 256 + + weight = torch.randn( + + vocab_size, embedding_dim, dtype=torch.float32, device='cuda' + + ) + + out = torch.zeros( + + sequence_length, embedding_dim, dtype=torch.float32, device='cuda' + + ) + + embedding(input_ids, weight, vob_start_id, vob_end_id, out) + + results['test_case_4'] = out.clone() + + + + return results + + + +result_gold = test_embedding() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/flash_decode2_phi.py b/reflexion_oneshot_tritonbench_iter1_4/exec/flash_decode2_phi.py new file mode 100644 index 0000000..3e9a502 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/flash_decode2_phi.py @@ -0,0 +1,144 @@ +import torch +import triton +import triton.language as tl + +@triton.jit +def _fwd_kernel_flash_decode_stage2(B_Seqlen, Mid_O, Mid_O_LogExpSum, Out, stride_mid_ob, stride_mid_oh, stride_mid_os, stride_mid_od, stride_mid_o_eb, stride_mid_o_eh, stride_mid_o_es, stride_obs, stride_oh, stride_od, head_dim, BLOCK_SEQ: tl.constexpr, BLOCK_DMODEL: tl.constexpr): + cur_batch = tl.program_id(0) + cur_head = tl.program_id(1) + offs_d = tl.arange(0, BLOCK_DMODEL) + cur_batch_seq_len = tl.load(B_Seqlen + cur_batch) + block_n_size = (cur_batch_seq_len + BLOCK_SEQ - 1) // BLOCK_SEQ + sum_exp = 0.0 + max_logic = -float('inf') + acc = tl.zeros([BLOCK_DMODEL], dtype=tl.float32) + for block_seq_n in range(0, block_n_size): + tv = tl.load(Mid_O + cur_batch * stride_mid_ob + cur_head * stride_mid_oh + block_seq_n * stride_mid_os + offs_d, mask=offs_d < head_dim, other=0.0) + tlogic = tl.load(Mid_O_LogExpSum + cur_batch * stride_mid_o_eb + cur_head * stride_mid_o_eh + block_seq_n) + new_max_logic = tl.maximum(tlogic, max_logic) + old_scale = tl.exp(max_logic - new_max_logic) + acc *= old_scale + exp_logic = tl.exp(tlogic - new_max_logic) + acc += exp_logic * tv + sum_exp = sum_exp * old_scale + exp_logic + max_logic = new_max_logic + tl.store(Out + cur_batch * stride_obs + cur_head * stride_oh + offs_d, acc / sum_exp, mask=offs_d < head_dim) + +@torch.no_grad() +def flash_decode_stage2(mid_out, mid_out_logexpsum, B_Seqlen, Out, block_seq): + Lk = mid_out.shape[-1] + head_dim = Lk + batch, head_num = (mid_out.shape[0], mid_out.shape[1]) + BLOCK_DMODEL = triton.next_power_of_2(head_dim) + grid = (batch, head_num) + _fwd_kernel_flash_decode_stage2[grid](B_Seqlen, mid_out, mid_out_logexpsum, Out, mid_out.stride(0), mid_out.stride(1), mid_out.stride(2), mid_out.stride(3), mid_out_logexpsum.stride(0), mid_out_logexpsum.stride(1), mid_out_logexpsum.stride(2), Out.stride(0), Out.stride(1), Out.stride(2), head_dim, BLOCK_SEQ=block_seq, BLOCK_DMODEL=BLOCK_DMODEL, num_warps=4, num_stages=2) + +################################################################################################################################################## + + + + + +import torch + + + +# Define the test function + +def test_flash_decode_stage2(): + + # Define the parameters for different test cases + + batch_size = 2 + + head_num = 4 + + seq_block_num = 3 + + head_dim = 64 + + block_seq = 16 + + + + test_cases = { + + "test_case_1": { + + "B_Seqlen": torch.randint(1, seq_block_num * block_seq, (batch_size,), dtype=torch.int32, device='cuda'), + + "mid_out": torch.randn((batch_size, head_num, seq_block_num, head_dim), dtype=torch.float32, device='cuda'), + + "mid_out_logexpsum": torch.randn((batch_size, head_num, seq_block_num), dtype=torch.float32, device='cuda'), + + "Out": torch.zeros((batch_size, head_num, head_dim), dtype=torch.float32, device='cuda'), + + "block_seq": block_seq + + }, + + "test_case_2": { + + "B_Seqlen": torch.randint(1, seq_block_num * block_seq, (batch_size,), dtype=torch.int32, device='cuda'), + + "mid_out": torch.randn((batch_size, head_num, seq_block_num, head_dim), dtype=torch.float32, device='cuda'), + + "mid_out_logexpsum": torch.randn((batch_size, head_num, seq_block_num), dtype=torch.float32, device='cuda'), + + "Out": torch.zeros((batch_size, head_num, head_dim), dtype=torch.float32, device='cuda'), + + "block_seq": block_seq + 1 # Different block size + + }, + + "test_case_3": { + + "B_Seqlen": torch.randint(1, seq_block_num * block_seq, (batch_size,), dtype=torch.int32, device='cuda'), + + "mid_out": torch.randn((batch_size, head_num, seq_block_num, head_dim), dtype=torch.float32, device='cuda'), + + "mid_out_logexpsum": torch.randn((batch_size, head_num, seq_block_num), dtype=torch.float32, device='cuda'), + + "Out": torch.zeros((batch_size, head_num, head_dim), dtype=torch.float32, device='cuda'), + + "block_seq": block_seq // 2 # Different block size + + }, + + "test_case_4": { + + "B_Seqlen": torch.randint(1, seq_block_num * block_seq, (batch_size,), dtype=torch.int32, device='cuda'), + + "mid_out": torch.randn((batch_size, head_num, seq_block_num, head_dim), dtype=torch.float32, device='cuda'), + + "mid_out_logexpsum": torch.randn((batch_size, head_num, seq_block_num), dtype=torch.float32, device='cuda'), + + "Out": torch.zeros((batch_size, head_num, head_dim), dtype=torch.float32, device='cuda'), + + "block_seq": block_seq * 2 # Different block size + + } + + } + + + + # Execute the function for all test cases + + results = {} + + for key, test_case in test_cases.items(): + + flash_decode_stage2(test_case["mid_out"], test_case["mid_out_logexpsum"], test_case["B_Seqlen"], test_case["Out"], test_case["block_seq"]) + + results[key] = test_case["Out"] + + + + return results + + + +# Run the test + +result_gold = test_flash_decode_stage2() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/efficiency.json b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/efficiency.json new file mode 100644 index 0000000..aad042f --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/efficiency.json @@ -0,0 +1,20 @@ +{ + "speed_up": [ + 3.2874, + 4.8564, + 2.4925, + 2.0037, + 1.809, + 5.3753, + 0.6082 + ], + "efficiency": [ + 88.5857, + 67.5919, + 1.5125, + 1.4315, + 1.076, + 72.3781, + 13.8734 + ] +} \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/embedding_triton_kernel.json b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/embedding_triton_kernel.json new file mode 100644 index 0000000..9b9e3bf --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/embedding_triton_kernel.json @@ -0,0 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a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/logs/l2_norm_triton1_perf.py.err b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/logs/l2_norm_triton1_perf.py.err new file mode 100644 index 0000000..e69de29 diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/logs/l2_norm_triton1_perf.py.log b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/logs/l2_norm_triton1_perf.py.log new file mode 100644 index 0000000..11740f6 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/logs/l2_norm_triton1_perf.py.log @@ -0,0 +1,11 @@ +{'input_size': [torch.Size([16])], 'ms': 0.005973000079393387, 'GB/s': 0.021429767001275444, 'TFLOPS': 5.35744175031886e-06} +{'input_size': [torch.Size([32])], 'ms': 0.005934000015258789, 'GB/s': 0.04314121997669654, 'TFLOPS': 1.0785304994174135e-05} +{'input_size': [torch.Size([64])], 'ms': 0.005934000015258789, 'GB/s': 0.08628243995339308, 'TFLOPS': 2.157060998834827e-05} +{'input_size': [torch.Size([128])], 'ms': 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a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/logs/matrix_transpose_perf.py.err b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/logs/matrix_transpose_perf.py.err new file mode 100644 index 0000000..e69de29 diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/logs/matrix_transpose_perf.py.log b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/logs/matrix_transpose_perf.py.log new file mode 100644 index 0000000..b801712 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/logs/matrix_transpose_perf.py.log @@ -0,0 +1,8 @@ +{'input_size': [torch.Size([128, 4])], 'ms': 0.008098999969661236, 'GB/s': 0.25287072572808805, 'TFLOPS': 0} +{'input_size': [torch.Size([128, 8])], 'ms': 0.00837900023907423, 'GB/s': 0.488841136547402, 'TFLOPS': 0} +{'input_size': [torch.Size([128, 16])], 'ms': 0.008138000033795834, 'GB/s': 1.0066355328065757, 'TFLOPS': 0} +{'input_size': [torch.Size([128, 32])], 'ms': 0.00837900023907423, 'GB/s': 1.955364546189608, 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0.006134000141173601, 'GB/s': 5.342027917483971, 'TFLOPS': 0.0006677534896854965} +{'input_size': [torch.Size([8192])], 'ms': 0.006134000141173601, 'GB/s': 10.684055834967943, 'TFLOPS': 0.001335506979370993} +{'input_size': [torch.Size([16384])], 'ms': 0.006173999980092049, 'GB/s': 21.229672889964252, 'TFLOPS': 0.0026537091112455316} +{'input_size': [torch.Size([32768])], 'ms': 0.006213999819010496, 'GB/s': 42.186032770394135, 'TFLOPS': 0.005273254096299267} +{'input_size': [torch.Size([65536])], 'ms': 0.006213999819010496, 'GB/s': 84.37206554078827, 'TFLOPS': 0.010546508192598534} +{'input_size': [torch.Size([131072])], 'ms': 0.006254000123590231, 'GB/s': 167.66485117976694, 'TFLOPS': 0.020958106397470866} +{'input_size': [torch.Size([262144])], 'ms': 0.006415000185370445, 'GB/s': 326.913786344481, 'TFLOPS': 0.04086422329306013} +{'input_size': [torch.Size([524288])], 'ms': 0.00661499984562397, 'GB/s': 634.0595763996372, 'TFLOPS': 0.07925744704995465} +{'input_size': 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diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/logs/triton_matmul_perf.py.err b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/logs/triton_matmul_perf.py.err new file mode 100644 index 0000000..e69de29 diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/logs/triton_matmul_perf.py.log b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/logs/triton_matmul_perf.py.log new file mode 100644 index 0000000..20eeacd --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/logs/triton_matmul_perf.py.log @@ -0,0 +1,31 @@ +{'input_size': [torch.Size([256, 256]), torch.Size([256, 256])], 'ms': 0.023374000564217567, 'GB/s': 16.82279415197587, 'TFLOPS': 1.4355451009686078} +{'input_size': [torch.Size([384, 384]), torch.Size([384, 384])], 'ms': 0.02313300035893917, 'GB/s': 38.24562254234851, 'TFLOPS': 4.895439685420609} +{'input_size': [torch.Size([512, 512]), torch.Size([512, 512])], 'ms': 0.03299500048160553, 'GB/s': 47.66976745088578, 'TFLOPS': 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] + ], + "ms": 0.337007999420166, + "GB/s": 3186.101890303524, + "TFLOPS": 0.39826273628794057 + } +] \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/embedding_triton_kernel_perf.py b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/embedding_triton_kernel_perf.py new file mode 100644 index 0000000..64d1cb3 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/embedding_triton_kernel_perf.py @@ -0,0 +1,56 @@ +import sys +import os + +sys.path.append('/workspace/reflexion_oneshot_tritonbench_iter1_4/exec') +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +from embedding_triton_kernel import embedding +from performance_utils import Performance_Metrics, do_bench_config + +import torch +import triton +import triton.language as tl + +class performance_metrics(Performance_Metrics): + def __init__(self, dtype=None, is_backward=False, **kwargs): + super().__init__('embedding_triton_kernel', dtype=dtype, is_backward=is_backward, **kwargs) + + def get_input_tensors(self): + self.input_tensors = [] + vocab_size = 10000 # Example vocabulary size + hidden_size = 768 # Example hidden size + for i in range(2, 18): # Example range for input sizes + seq_length = 2 ** i + input_ids = torch.randint(0, vocab_size, (seq_length,), dtype=torch.int32) + weight = torch.rand(vocab_size, hidden_size, dtype=torch.float16) + out = torch.zeros(seq_length, hidden_size, dtype=torch.float16) + vob_start_id = 0 + vob_end_id = vocab_size + self.input_tensors.append((input_ids, weight, vob_start_id, vob_end_id, out)) + + def to_cuda(self, input_tensor): + input_ids, weight, vob_start_id, vob_end_id, out = input_tensor + return (input_ids.cuda(), weight.cuda(), vob_start_id, vob_end_id, out.cuda()) + + def call_op(self, input_tensor): + input_ids, weight, vob_start_id, vob_end_id, out = input_tensor + embedding(input_ids, weight, vob_start_id, vob_end_id, out) + return out + + def get_gbps(self, input_tensor, runtime): + input_ids, weight, _, _, _ = input_tensor + total_bytes = input_ids.numel() * input_ids.element_size() + weight.numel() * weight.element_size() + GBPS = total_bytes / (runtime / 1000) / 1e9 + return GBPS + + def get_tflops(self, input_tensor, runtime): + input_ids, weight, _, _, _ = input_tensor + FLOPS = 2 * input_ids.numel() * weight.shape[1] # Assuming 2 FLOPS per element (load and store) + TFLOPS = FLOPS / (runtime / 1000) / 1e12 + return TFLOPS + +if __name__ == '__main__': + op_perf = performance_metrics() + op_perf.get_input_tensors() + op_perf.get_do_bench_config(warmup=100, rep=1000) + op_perf.run_benchmark() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/flash_decode2_phi_perf.py b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/flash_decode2_phi_perf.py new file mode 100644 index 0000000..c8d5e65 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/flash_decode2_phi_perf.py @@ -0,0 +1,59 @@ +import sys +import os + +sys.path.append('/workspace/reflexion_oneshot_tritonbench_iter1_4/exec') +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +from flash_decode2_phi import flash_decode_stage2 +from performance_utils import Performance_Metrics, do_bench_config + +import torch +import triton +import triton.language as tl + +class performance_metrics(Performance_Metrics): + def __init__(self, dtype=None, is_backward=False, **kwargs): + super().__init__('flash_decode2_phi', dtype=dtype, is_backward=is_backward, **kwargs) + + def get_input_tensors(self): + self.input_tensors = [] + for i in range(2, 18): # Adjust the range as needed for your testing + batch_size = 2 ** i + head_num = 8 # Example head number, adjust as needed + seq_block_num = 16 # Example sequence block number, adjust as needed + head_dim = 64 # Example head dimension, adjust as needed + + mid_out = torch.rand(batch_size, head_num, seq_block_num, head_dim, dtype=torch.float32) + mid_out_logexpsum = torch.rand(batch_size, head_num, seq_block_num, dtype=torch.float32) + B_Seqlen = torch.randint(1, seq_block_num * 32, (batch_size,), dtype=torch.int32) + Out = torch.empty(batch_size, head_num, head_dim, dtype=torch.float32) + + self.input_tensors.append((mid_out, mid_out_logexpsum, B_Seqlen, Out)) + + def to_cuda(self, input_tensor): + mid_out, mid_out_logexpsum, B_Seqlen, Out = input_tensor + return (mid_out.cuda(), mid_out_logexpsum.cuda(), B_Seqlen.cuda(), Out.cuda()) + + def call_op(self, input_tensor): + mid_out, mid_out_logexpsum, B_Seqlen, Out = input_tensor + block_seq = 32 # Example block sequence size, adjust as needed + flash_decode_stage2(mid_out, mid_out_logexpsum, B_Seqlen, Out, block_seq) + return Out + + def get_gbps(self, input_tensor, runtime): + mid_out, mid_out_logexpsum, B_Seqlen, Out = input_tensor + total_bytes = mid_out.numel() * mid_out.element_size() + mid_out_logexpsum.numel() * mid_out_logexpsum.element_size() + B_Seqlen.numel() * B_Seqlen.element_size() + Out.numel() * Out.element_size() + GBPS = total_bytes / (runtime / 1000) / 1e9 + return GBPS + + def get_tflops(self, input_tensor, runtime): + mid_out, _, _, _ = input_tensor + FLOPS = 2 * mid_out.numel() # Example calculation, adjust based on actual operations + TFLOPS = FLOPS / (runtime / 1000) / 1e12 + return TFLOPS + +if __name__ == '__main__': + op_perf = performance_metrics() + op_perf.get_input_tensors() + op_perf.get_do_bench_config(warmup=100, rep=1000) + op_perf.run_benchmark() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/l2_norm_bwd_perf.py b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/l2_norm_bwd_perf.py new file mode 100644 index 0000000..2d9790d --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/l2_norm_bwd_perf.py @@ -0,0 +1,51 @@ +import sys +import os + +sys.path.append('/workspace/reflexion_oneshot_tritonbench_iter1_4/exec') +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +from l2_norm_bwd import _l2_norm_bwd +from performance_utils import Performance_Metrics, do_bench_config + +import torch +import triton +import triton.language as tl + +class performance_metrics(Performance_Metrics): + def __init__(self, dtype=None, is_backward=False, **kwargs): + super().__init__('l2_norm_bwd', dtype=dtype, is_backward=is_backward, **kwargs) + + def get_input_tensors(self): + self.input_tensors = [] + for i in range(4, 15): + size = 2 ** i + x = torch.rand(size, dtype=torch.float32) + dy = torch.rand(size, dtype=torch.float32) + self.input_tensors.append((x, dy)) + + def to_cuda(self, input_tensor): + x, dy = input_tensor + return x.cuda(), dy.cuda() + + def call_op(self, input_tensor): + x, dy = input_tensor + return _l2_norm_bwd(x, dy) + + def get_gbps(self, input_tensor, runtime): + x, dy = input_tensor + total_bytes = (x.numel() + dy.numel() + x.numel()) * x.element_size() # x, dy, and dx + GBPS = total_bytes / (runtime / 1000) / 1e9 + return GBPS + + def get_tflops(self, input_tensor, runtime): + x, dy = input_tensor + # Assuming each element involves a few FLOPs, e.g., multiplication, addition + FLOPS = 2 * x.numel() # Simplified estimation + TFLOPS = FLOPS / (runtime / 1000) / 1e12 + return TFLOPS + +if __name__ == '__main__': + op_perf = performance_metrics() + op_perf.get_input_tensors() + op_perf.get_do_bench_config(warmup=100, rep=1000) + op_perf.run_benchmark() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/l2_norm_triton1_perf.py b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/l2_norm_triton1_perf.py new file mode 100644 index 0000000..bd9e343 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/l2_norm_triton1_perf.py @@ -0,0 +1,75 @@ +import sys +import os +import json + +sys.path.append('/workspace/reflexion_oneshot_tritonbench_iter1_4/exec') +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +from l2_norm_triton1 import _l2_norm_fwd +from performance_utils import Performance_Metrics, do_bench_config + +import torch +import triton +import triton.language as tl + +class performance_metrics(Performance_Metrics): + def __init__(self, dtype=None, is_backward=False, **kwargs): + super().__init__('l2_norm_triton1', dtype=dtype, is_backward=is_backward, **kwargs) + + def get_input_tensors(self): + self.input_tensors = [] + for i in range(4, 15): + size = 2 ** i + input_tensor = torch.rand(size, dtype=torch.float32) + self.input_tensors.append(input_tensor) + + def to_cuda(self, input_tensor): + return input_tensor.cuda() + + def call_op(self, input_tensor): + return _l2_norm_fwd(input_tensor) + + def get_gbps(self, input_tensor, runtime): + x = input_tensor + total_bytes = 2 * x.numel() * x.element_size() # Read and write + GBPS = total_bytes / (runtime / 1000) / 1e9 + return GBPS + + def get_tflops(self, input_tensor, runtime): + x = input_tensor + FLOPS = 2 * x.numel() # Each element involves a multiply and an add + TFLOPS = FLOPS / (runtime / 1000) / 1e12 + return TFLOPS + + def run_benchmark(self): + results = [] + for input_tensor_ in self.input_tensors: + try: + input_tensor = self.to_cuda(input_tensor_) + # print(input_tensor) + op = lambda : self.call_op(input_tensor) + ms = self.get_runtime(op) + gbps = self.get_gbps(input_tensor, ms) + tflops = self.get_tflops(input_tensor, ms) + result = { + "input_size": [input_tensor.shape], + "ms": ms, + "GB/s": gbps, + "TFLOPS": tflops + } + print(result) + results.append(result) + except Exception as e: + print(f"Failed to run benchmark for input tensor. Error: {e}") + input_tensor = None + folder_path = "/workspace/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf" + file_name = self.op_name + ".json" + file_path = os.path.join(folder_path, file_name) + with open(file_path, 'w', encoding='utf8') as f: + json.dump(results, f, indent=4) + +if __name__ == '__main__': + op_perf = performance_metrics() + op_perf.get_input_tensors() + op_perf.get_do_bench_config(warmup=100, rep=1000) + op_perf.run_benchmark() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/matrix_transpose_perf.py b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/matrix_transpose_perf.py new file mode 100644 index 0000000..b67c397 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/matrix_transpose_perf.py @@ -0,0 +1,76 @@ +import sys +import os +import json + +sys.path.append('/workspace/reflexion_oneshot_tritonbench_iter1_4/exec') +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +from matrix_transpose import wrapper +from performance_utils import Performance_Metrics, do_bench_config + +import torch +import triton +import triton.language as tl + +class performance_metrics(Performance_Metrics): + def __init__(self, dtype=None, is_backward=False, **kwargs): + super().__init__('matrix_transpose', dtype=dtype, is_backward=is_backward, **kwargs) + + def get_input_tensors(self): + self.input_tensors = [] + for i in range(2, 10): # Adjust the range as needed for testing + size_m = 128 + d_head = 2 ** i + input_tensor = torch.randn((size_m, d_head), dtype=torch.float16) + self.input_tensors.append(input_tensor) + + def to_cuda(self, input_tensor): + return input_tensor.cuda() + + def call_op(self, input_tensor): + return wrapper(input_tensor.size(0), input_tensor.size(1)) + + def get_gbps(self, input_tensor, runtime): + size_m, d_head = input_tensor.size() + total_bytes = 2 * size_m * d_head * 2 # 2 bytes per float16 element + GBPS = total_bytes / (runtime / 1000) / 1e9 + return GBPS + + def get_tflops(self, input_tensor, runtime): + size_m, d_head = input_tensor.size() + # Transpose operation doesn't involve floating point operations, so TFLOPS is 0 + TFLOPS = 0 + return TFLOPS + + def run_benchmark(self): + results = [] + for input_tensor_ in self.input_tensors: + try: + input_tensor = self.to_cuda(input_tensor_) + # print(input_tensor) + op = lambda : self.call_op(input_tensor) + ms = self.get_runtime(op) + gbps = self.get_gbps(input_tensor, ms) + tflops = self.get_tflops(input_tensor, ms) + result = { + "input_size": [input_tensor.shape], + "ms": ms, + "GB/s": gbps, + "TFLOPS": tflops + } + print(result) + results.append(result) + except Exception as e: + print(f"Failed to run benchmark for input tensor. Error: {e}") + input_tensor = None + folder_path = "/workspace/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf" + file_name = self.op_name + ".json" + file_path = os.path.join(folder_path, file_name) + with open(file_path, 'w', encoding='utf8') as f: + json.dump(results, f, indent=4) + +if __name__ == '__main__': + op_perf = performance_metrics() + op_perf.get_input_tensors() + op_perf.get_do_bench_config(warmup=100, rep=1000) + op_perf.run_benchmark() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/matrix_vector_multip_perf.py b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/matrix_vector_multip_perf.py new file mode 100644 index 0000000..7214774 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/matrix_vector_multip_perf.py @@ -0,0 +1,52 @@ +import sys +import os + +sys.path.append('/workspace/reflexion_oneshot_tritonbench_iter1_4/exec') +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +from matrix_vector_multip import mv +from performance_utils import Performance_Metrics, do_bench_config + +import torch +import triton +import triton.language as tl + +class performance_metrics(Performance_Metrics): + def __init__(self, dtype=None, is_backward=False, **kwargs): + super().__init__('matrix_vector_multip', dtype=dtype, is_backward=is_backward, **kwargs) + + def get_input_tensors(self): + self.input_tensors = [] + for i in range(2, 20): # Adjust the range for different sizes + M = 128 * i + N = 128 * (i - 1) # Example: N is half of M + matrix = torch.rand((N, M), dtype=torch.float32) + vector = torch.rand((M,), dtype=torch.float32) + self.input_tensors.append((matrix, vector)) + + def to_cuda(self, input_tensor): + matrix, vector = input_tensor + return (matrix.cuda(), vector.cuda()) + + def call_op(self, input_tensor): + matrix, vector = input_tensor + return mv(matrix, vector) + + def get_gbps(self, input_tensor, runtime): + matrix, vector = input_tensor + total_bytes = (matrix.numel() + vector.numel() + matrix.size(0)) * matrix.element_size() + GBPS = total_bytes / (runtime / 1000) / 1e9 + return GBPS + + def get_tflops(self, input_tensor, runtime): + matrix, vector = input_tensor + N, M = matrix.shape + FLOPS = 2 * N * M # Each element in the output involves M multiplications and M-1 additions + TFLOPS = FLOPS / (runtime / 1000) / 1e12 + return TFLOPS + +if __name__ == '__main__': + op_perf = performance_metrics() + op_perf.get_input_tensors() + op_perf.get_do_bench_config(warmup=100, rep=1000) + op_perf.run_benchmark() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/performance_utils.py b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/performance_utils.py new file mode 100644 index 0000000..ac472ec --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/performance_utils.py @@ -0,0 +1,145 @@ +# Modifications Copyright(C)[2025] Advanced Micro Devices, Inc. All rights reserved. +# https://github.com/thunlp/TritonBench - Apache License 2.0 +import torch +import triton +import triton.language as tl + +from typing import Callable +import json +import os + +class do_bench_config(): + def __init__( + self, + warm_up=25, + repetition=100, + grad_to_none=None, + quantiles=[0.5, 0.8, 0.2], + return_mode="median" + ): + self.warm_up = warm_up + self.repetition = repetition + self.grad_to_none = grad_to_none + self.quantiles = quantiles + self.return_mode = return_mode + +class Performance_Metrics: + def __init__( + self, + op_name, + dtype=None, + is_backward=False, + **kwargs + ): + self.op_name = op_name + self.dtype = dtype + if is_backward: + self.op_name += 'backward' + self.kwargs = kwargs + + self.input_tensors = [] + self.do_bench_config = do_bench_config() + + def get_input_tensors(self): + raise NotImplementedError("You must implement this method to get input tensors") + + def to_cuda(self, input_tensor): + raise NotImplementedError("You must implement this method to get input tensors") + + def call_op(self, input_tensor): + raise NotImplementedError("You must implement this method to call the op") + + def get_do_bench_config(self, warmup=None, rep=None): + if warmup != None and rep != None: + self.do_bench_config = do_bench_config( + warm_up=warmup, + repetition=rep, + ) + return + + if self.input_tensors == []: + raise NotImplementedError("You must implement this method to get input_tensors") + + previous_ms = None + epsilon = 1e-4 + stable_count = 0 + max_stable_count = 3 + input_tensor = self.to_cuda(self.input_tensors[-1]) + + for t in range(1, 11): + warmup = 100 * t + rep = 1000 * t + + ms, min_ms, max_ms = triton.testing.do_bench( + lambda: self.call_op(input_tensor), + warmup=warmup, + rep=rep, + quantiles=[0.5, 0.8, 0.2], + return_mode="median" + ) + + print("warmup time:", warmup, "rep time:", rep, "runtime:", ms) + + if previous_ms is not None: + relative_change = abs(ms - previous_ms) / abs(previous_ms) if previous_ms != 0 else float('inf') + + if relative_change < epsilon: + stable_count += 1 + else: + stable_count = 0 + + if stable_count >= max_stable_count: + print(f"MS stabilized with warmup={warmup} and rep={rep}") + self.do_bench_config = do_bench_config( + warm_up=warmup, + repetition=rep, + ) + return + + previous_ms = ms + + print("MS did not stabilize. Returning default config.") + raise NotImplementedError("You must implement this method to make the runtime stable") + + def get_runtime(self, op: Callable): + ms, min_ms, max_ms = triton.testing.do_bench( + op, + warmup=self.do_bench_config.warm_up, + rep=self.do_bench_config.repetition, + quantiles=self.do_bench_config.quantiles, + return_mode=self.do_bench_config.return_mode + ) + return ms + + def get_gbps(self, input_tensor, runtime): + raise NotImplementedError("You must implement this method to get the method to calculate GBPS") + + def get_tflops(self, input_tensor, runtime): + raise NotImplementedError("You must implement this method to get the method to calculate TFLOPS") + + def run_benchmark(self): + results = [] + for input_tensor_ in self.input_tensors: + try: + input_tensor = self.to_cuda(input_tensor_) + # print(input_tensor) + op = lambda : self.call_op(input_tensor) + ms = self.get_runtime(op) + gbps = self.get_gbps(input_tensor, ms) + tflops = self.get_tflops(input_tensor, ms) + result = { + "input_size": [item.shape if type(item)==torch.Tensor else item for item in input_tensor], + "ms": ms, + "GB/s": gbps, + "TFLOPS": tflops + } + print(result) + results.append(result) + except Exception as e: + print(f"Failed to run benchmark for input tensor. Error: {e}") + input_tensor = None + folder_path = "/workspace/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf" + file_name = self.op_name + ".json" + file_path = os.path.join(folder_path, file_name) + with open(file_path, 'w', encoding='utf8') as f: + json.dump(results, f, indent=4) diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/rotary_transform_perf.py b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/rotary_transform_perf.py new file mode 100644 index 0000000..6cb0b9b --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/rotary_transform_perf.py @@ -0,0 +1,56 @@ +import sys +import os + +sys.path.append('/workspace/reflexion_oneshot_tritonbench_iter1_4/exec') +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +from rotary_transform import apply_rotary +from performance_utils import Performance_Metrics, do_bench_config + +import torch +import triton +import triton.language as tl + +class performance_metrics(Performance_Metrics): + def __init__(self, dtype=None, is_backward=False, **kwargs): + super().__init__('rotary_transform', dtype=dtype, is_backward=is_backward, **kwargs) + + def get_input_tensors(self): + self.input_tensors = [] + for i in range(2, 16): # Choose a reasonable range for testing + batch_size = 2 ** i + seqlen = 128 # Fixed sequence length + nheads = 8 # Number of attention heads + headdim = 64 # Dimension of each head + rotary_dim = 32 # Rotary dimension + x = torch.rand(batch_size, seqlen, nheads, headdim, dtype=torch.float32) + cos = torch.rand(seqlen, rotary_dim // 2, dtype=torch.float32) + sin = torch.rand(seqlen, rotary_dim // 2, dtype=torch.float32) + self.input_tensors.append((x, cos, sin)) + + def to_cuda(self, input_tensor): + x, cos, sin = input_tensor + return (x.cuda(), cos.cuda(), sin.cuda()) + + def call_op(self, input_tensor): + x, cos, sin = input_tensor + return apply_rotary(x, cos, sin) + + def get_gbps(self, input_tensor, runtime): + x, cos, sin = input_tensor + total_bytes = x.numel() * x.element_size() + cos.numel() * cos.element_size() + sin.numel() * sin.element_size() + GBPS = total_bytes / (runtime / 1000) / 1e9 + return GBPS + + def get_tflops(self, input_tensor, runtime): + x, cos, sin = input_tensor + # Assuming each element in x is involved in a few operations (e.g., multiply and add) + FLOPS = 2 * x.numel() # Simplified estimation + TFLOPS = FLOPS / (runtime / 1000) / 1e12 + return TFLOPS + +if __name__ == '__main__': + op_perf = performance_metrics() + op_perf.get_input_tensors() + op_perf.get_do_bench_config(warmup=100, rep=1000) + op_perf.run_benchmark() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/sin_kernel_perf.py b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/sin_kernel_perf.py new file mode 100644 index 0000000..84b92ef --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/sin_kernel_perf.py @@ -0,0 +1,75 @@ +import sys +import os +import json +sys.path.append('/workspace/reflexion_oneshot_tritonbench_iter1_4/exec') +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +# Correctly import the kernel function +from sin_kernel import call_kernel +from performance_utils import Performance_Metrics, do_bench_config + +import torch +import triton +import triton.language as tl + +class performance_metrics(Performance_Metrics): + def __init__(self, dtype=None, is_backward=False, **kwargs): + super().__init__('sin_kernel', dtype=dtype, is_backward=is_backward, **kwargs) + + def get_input_tensors(self): + self.input_tensors = [] + for i in range(12, 28): + size = 2 ** i + input_tensor = torch.rand(size, dtype=torch.float32) + self.input_tensors.append(input_tensor) + + def to_cuda(self, input_tensor): + return input_tensor.cuda() + + def call_op(self, input_tensor): + return call_kernel(input_tensor) + + def get_gbps(self, input_tensor, runtime): + x = input_tensor + total_bytes = 2 * x.numel() * x.element_size() # Read and write + GBPS = total_bytes / (runtime / 1000) / 1e9 + return GBPS + + def get_tflops(self, input_tensor, runtime): + x = input_tensor + FLOPS = x.numel() # One sin operation per element + TFLOPS = FLOPS / (runtime / 1000) / 1e12 + return TFLOPS + + def run_benchmark(self): + results = [] + for input_tensor_ in self.input_tensors: + try: + input_tensor = self.to_cuda(input_tensor_) + # print(input_tensor) + op = lambda : self.call_op(input_tensor) + ms = self.get_runtime(op) + gbps = self.get_gbps(input_tensor, ms) + tflops = self.get_tflops(input_tensor, ms) + result = { + "input_size": [input_tensor.shape], + "ms": ms, + "GB/s": gbps, + "TFLOPS": tflops + } + print(result) + results.append(result) + except Exception as e: + print(f"Failed to run benchmark for input tensor. Error: {e}") + input_tensor = None + folder_path = "/workspace/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf" + file_name = self.op_name + ".json" + file_path = os.path.join(folder_path, file_name) + with open(file_path, 'w', encoding='utf8') as f: + json.dump(results, f, indent=4) + +if __name__ == '__main__': + op_perf = performance_metrics() + op_perf.get_input_tensors() + op_perf.get_do_bench_config(warmup=100, rep=1000) + op_perf.run_benchmark() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/triton_matmul_perf.py b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/triton_matmul_perf.py new file mode 100644 index 0000000..4861f0f --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/tmp/triton_matmul_perf.py @@ -0,0 +1,54 @@ +import sys +import os + +sys.path.append('/workspace/reflexion_oneshot_tritonbench_iter1_4/exec') +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +from triton_matmul import matmul # Correctly import the matmul function +from performance_utils import Performance_Metrics, do_bench_config + +import torch +import triton +import triton.language as tl + +class performance_metrics(Performance_Metrics): + def __init__(self, dtype=None, is_backward=False, **kwargs): + super().__init__('triton_matmul', dtype=dtype, is_backward=is_backward, **kwargs) + + def get_input_tensors(self): + self.input_tensors = [] + for i in range(2, 33): # Define a range for matrix sizes + M = N = K = 128 * i + a = torch.rand((M, K), dtype=torch.float16) # Use float16 for compatibility + b = torch.rand((K, N), dtype=torch.float16) + self.input_tensors.append((a, b)) + + def to_cuda(self, input_tensor): + a, b = input_tensor + return (a.cuda(), b.cuda()) + + def call_op(self, input_tensor): + a, b = input_tensor + return matmul(a, b) + + def get_gbps(self, input_tensor, runtime): + a, b = input_tensor + M, K = a.shape + K, N = b.shape + total_bytes = (M * K + K * N + M * N) * a.element_size() + GBPS = total_bytes / (runtime / 1000) / 1e9 + return GBPS + + def get_tflops(self, input_tensor, runtime): + a, b = input_tensor + M, K = a.shape + K, N = b.shape + FLOPS = 2 * M * N * K + TFLOPS = FLOPS / (runtime / 1000) / 1e12 + return TFLOPS + +if __name__ == '__main__': + op_perf = performance_metrics() + op_perf.get_input_tensors() + op_perf.get_do_bench_config(warmup=100, rep=1000) + op_perf.run_benchmark() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/triton_matmul.json b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/triton_matmul.json new file mode 100644 index 0000000..c377543 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/gen_perf/triton_matmul.json @@ -0,0 +1,467 @@ +[ + { + "input_size": [ + [ + 256, + 256 + ], + [ + 256, + 256 + ] + ], + "ms": 0.023374000564217567, + "GB/s": 16.82279415197587, + "TFLOPS": 1.4355451009686078 + }, + { + "input_size": [ + [ + 384, + 384 + ], + [ + 384, + 384 + ] + ], + "ms": 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"ms": 0.09036599844694138, + "GB/s": 244.76462806955954, + "TFLOPS": 156.64936196451814 + }, + { + "input_size": [ + [ + 2048, + 2048 + ], + [ + 2048, + 2048 + ] + ], + "ms": 0.10375600308179855, + "GB/s": 242.54812495195978, + "TFLOPS": 165.57951996720453 + }, + { + "input_size": [ + [ + 2176, + 2176 + ], + [ + 2176, + 2176 + ] + ], + "ms": 0.10768499970436096, + "GB/s": 263.8237087616343, + "TFLOPS": 191.36013008843872 + }, + { + "input_size": [ + [ + 2304, + 2304 + ], + [ + 2304, + 2304 + ] + ], + "ms": 0.12047400325536728, + "GB/s": 264.37650563073674, + "TFLOPS": 203.04115632440582 + }, + { + "input_size": [ + [ + 2432, + 2432 + ], + [ + 2432, + 2432 + ] + ], + "ms": 0.1268489956855774, + "GB/s": 279.76369704939594, + "TFLOPS": 226.79510374137698 + }, + { + "input_size": [ + [ + 2560, + 2560 + ], + [ + 2560, + 2560 + ] + ], + "ms": 0.1367110013961792, + "GB/s": 287.62571847490653, + "TFLOPS": 245.44061309858694 + }, + { + "input_size": [ + [ + 2688, + 2688 + ], + [ + 2688, + 2688 + ] + ], + "ms": 0.1428859978914261, + "GB/s": 303.4031650388981, + "TFLOPS": 271.84923587485275 + }, + { + "input_size": [ + [ + 2816, + 2816 + ], + [ + 2816, + 2816 + ] + ], + "ms": 0.15852099657058716, + "GB/s": 300.14406311667165, + "TFLOPS": 281.7352272455158 + }, + { + "input_size": [ + [ + 2944, + 2944 + ], + [ + 2944, + 2944 + ] + ], + "ms": 0.16493499279022217, + "GB/s": 315.29280185037163, + "TFLOPS": 309.4073362158314 + }, + { + "input_size": [ + [ + 3072, + 3072 + ], + [ + 3072, + 3072 + ] + ], + "ms": 0.18469999730587006, + "GB/s": 306.56797415232245, + "TFLOPS": 313.9256055319782 + }, + { + "input_size": [ + [ + 3200, + 3200 + ], + [ + 3200, + 3200 + ] + ], + "ms": 0.3279860019683838, + "GB/s": 187.32506762871702, + "TFLOPS": 199.81340547063147 + }, + { + "input_size": [ + [ + 3328, + 3328 + ], + [ + 3328, + 3328 + ] + ], + "ms": 0.3629460036754608, + "GB/s": 183.09473951233093, + "TFLOPS": 203.11309769901243 + }, + { + "input_size": [ + [ + 3456, + 3456 + ], + [ + 3456, + 3456 + ] + ], + "ms": 0.35589098930358887, + "GB/s": 201.36395175453052, + "TFLOPS": 231.97127242121917 + }, + { + "input_size": [ + [ + 3584, + 3584 + ], + [ + 3584, + 3584 + ] + ], + "ms": 0.3928140103816986, + "GB/s": 196.200578296865, + "TFLOPS": 234.39429087198806 + }, + { + "input_size": [ + [ + 3712, + 3712 + ], + [ + 3712, + 3712 + ] + ], + "ms": 0.38976699113845825, + "GB/s": 212.11048108132778, + "TFLOPS": 262.4513685912963 + }, + { + "input_size": [ + [ + 3840, + 3840 + ], + [ + 3840, + 3840 + ] + ], + "ms": 0.4202769994735718, + "GB/s": 210.51259076946815, + "TFLOPS": 269.4561161849192 + }, + { + "input_size": [ + [ + 3968, + 3968 + ], + [ + 3968, + 3968 + ] + ], + "ms": 0.4224419891834259, + "GB/s": 223.62867901131085, + "TFLOPS": 295.7861994389605 + }, + { + "input_size": [ + [ + 4096, + 4096 + ], + [ + 4096, + 4096 + ] + ], + "ms": 0.4547550082206726, + "GB/s": 221.357201526745, + "TFLOPS": 302.2263658178491 + } +] \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/l2_norm_bwd.py b/reflexion_oneshot_tritonbench_iter1_4/exec/l2_norm_bwd.py new file mode 100644 index 0000000..0608484 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/l2_norm_bwd.py @@ -0,0 +1,108 @@ +import torch +import triton +import triton.language as tl + +@triton.jit +def _l2_norm_bwd_kernel(X, DY, DX, stride_x_row, N, eps, BLOCK_N: tl.constexpr): + row = tl.program_id(0) + X += row * stride_x_row + DX += row * stride_x_row + DY += row * stride_x_row + cols = tl.arange(0, BLOCK_N) + mask = cols < N + x = tl.load(X + cols, mask=mask, other=0.0).to(tl.float32) + dy = tl.load(DY + cols, mask=mask, other=0.0).to(tl.float32) + var = tl.sum(x * x) + rstd = 1 / tl.math.sqrt(var + eps) + dx = dy * rstd - tl.sum(dy * x) * (1 / (var + eps)) * rstd * x + tl.store(DX + cols, dx, mask=mask) + +def _l2_norm_bwd(x: torch.Tensor, dy: torch.Tensor, eps: float=1e-05): + x_shape_og = x.shape + x = x.reshape(-1, dy.shape[-1]) + dy = dy.reshape(-1, dy.shape[-1]) + if dy.stride(-1) != 1: + dy = dy.contiguous() + dx = torch.empty_like(x) + N = x.shape[-1] + M = x.shape[0] + MAX_FUSED_SIZE = 65536 // x.element_size() + BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N)) + if N > BLOCK_N: + raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.") + _l2_norm_bwd_kernel[M,](x, dy, dx, x.stride(0), N, eps, BLOCK_N=BLOCK_N) + return dx.reshape(x_shape_og) + +################################################################################################################################################## + + + + + +import torch + + + +# Test the backward L2 normalization + +def test_l2_norm_bwd(): + + results = {} + + + + # Test case 1: Default case + + x = torch.randn(4, 8, device='cuda', dtype=torch.float32) + + dy = torch.randn(4, 8, device='cuda', dtype=torch.float32) + + dx = _l2_norm_bwd(x, dy) + + results['test_case_1'] = dx + + + + # Test case 2: Different shape + + x = torch.randn(2, 16, device='cuda', dtype=torch.float32) + + dy = torch.randn(2, 16, device='cuda', dtype=torch.float32) + + dx = _l2_norm_bwd(x, dy) + + results['test_case_2'] = dx + + + + # Test case 3: Larger tensor + + x = torch.randn(8, 8, device='cuda', dtype=torch.float32) + + dy = torch.randn(8, 8, device='cuda', dtype=torch.float32) + + dx = _l2_norm_bwd(x, dy) + + results['test_case_3'] = dx + + + + # Test case 4: Edge case with small tensor + + x = torch.randn(1, 8, device='cuda', dtype=torch.float32) + + dy = torch.randn(1, 8, device='cuda', dtype=torch.float32) + + dx = _l2_norm_bwd(x, dy) + + results['test_case_4'] = dx + + + + return results + + + +# Run the tests + +result_gold = test_l2_norm_bwd() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/l2_norm_triton1.py b/reflexion_oneshot_tritonbench_iter1_4/exec/l2_norm_triton1.py new file mode 100644 index 0000000..d772e24 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/l2_norm_triton1.py @@ -0,0 +1,96 @@ +import torch +import triton +import triton.language as tl + +@triton.jit +def _l2_norm_fwd_1pass_kernel(X, Y, stride_x_row, N, eps, BLOCK_N: tl.constexpr): + row = tl.program_id(0) + X += row * stride_x_row + Y += row * stride_x_row + cols = tl.arange(0, BLOCK_N) + mask = cols < N + x = tl.load(X + cols, mask=mask, other=0.0).to(tl.float32) + var = tl.sum(x * x, axis=0) + rstd = 1 / tl.sqrt(var + eps) + y = x * rstd + tl.store(Y + cols, y, mask=mask) + +def _l2_norm_fwd(x, eps=1e-06): + x_shape_og = x.shape + x = x.reshape(-1, x.shape[-1]) + if x.stride(-1) != 1: + x = x.contiguous() + y = torch.empty_like(x) + N = x.shape[-1] + M = x.shape[0] + MAX_FUSED_SIZE = 65536 // x.element_size() + BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N)) + if N > BLOCK_N: + raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.") + grid = (M,) + _l2_norm_fwd_1pass_kernel[grid](x, y, stride_x_row=x.stride(0), N=N, eps=eps, BLOCK_N=BLOCK_N) + return y.reshape(x_shape_og) + +################################################################################################################################################## + + + + + +import torch + + + +# Test the forward L2 normalization + +def test_l2_norm_fwd(): + + results = {} + + + + # Test case 1 + + x1 = torch.randn(4, 8, device='cuda', dtype=torch.float32) + + y1 = _l2_norm_fwd(x1) + + results['test_case_1'] = y1 + + + + # Test case 2: Different batch size + + x2 = torch.randn(2, 8, device='cuda', dtype=torch.float32) + + y2 = _l2_norm_fwd(x2) + + results['test_case_2'] = y2 + + + + # Test case 3: Different feature size + + x3 = torch.randn(4, 4, device='cuda', dtype=torch.float32) + + y3 = _l2_norm_fwd(x3) + + results['test_case_3'] = y3 + + + + # Test case 4: Larger tensor + + x4 = torch.randn(8, 8, device='cuda', dtype=torch.float32) + + y4 = _l2_norm_fwd(x4) + + results['test_case_4'] = y4 + + + + return results + + + +result_gold = test_l2_norm_fwd() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/matrix_transpose.py b/reflexion_oneshot_tritonbench_iter1_4/exec/matrix_transpose.py new file mode 100644 index 0000000..1f125a0 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/matrix_transpose.py @@ -0,0 +1,74 @@ +import torch +import triton +import triton.language as tl + +@triton.jit +def kernel(M, Out, matrix_stridex, matrix_stridey, out_stridex, out_stridey, SIZE_M, D_HEAD, BLOCK_M: tl.constexpr, BLOCK_D: tl.constexpr): + pid_m = tl.program_id(0) + pid_d = tl.program_id(1) + offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + offs_d = pid_d * BLOCK_D + tl.arange(0, BLOCK_D) + mask_m = offs_m < SIZE_M + mask_d = offs_d < D_HEAD + inp_ptrs = M + offs_m[:, None] * matrix_stridex + offs_d[None, :] * matrix_stridey + out_ptrs = Out + offs_d[:, None] * out_stridex + offs_m[None, :] * out_stridey + tile = tl.load(inp_ptrs, mask=mask_m[:, None] & mask_d[None, :], other=0.0) + tl.store(out_ptrs, tile.T, mask=mask_d[:, None] & mask_m[None, :]) + +def wrapper(size_m: int, d_head: int) -> torch.Tensor: + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + matrix = torch.randn((size_m, d_head), dtype=torch.float16, device=device) + out = torch.empty((d_head, size_m), dtype=torch.float16, device=device) + BLOCK_M = 16 + BLOCK_D = 16 + grid = (triton.cdiv(size_m, BLOCK_M), triton.cdiv(d_head, BLOCK_D)) + kernel[grid](matrix, out, matrix.stride(0), matrix.stride(1), out.stride(0), out.stride(1), size_m, d_head, BLOCK_M=BLOCK_M, BLOCK_D=BLOCK_D) + return out + +################################################################################################################################################## + + + + + +import torch + + + +def test_triton_vs_torch(): + + results = {} + + + + # 测试用例 1: 基本矩阵转置 (小矩阵) + + size_m, d_head = 16, 16 + + out = wrapper(size_m, d_head) + + results["test_case_1"] = out.clone() + + + + # 测试用例 2: 非方形矩阵 + + size_m, d_head = 32, 64 + + out = wrapper(size_m, d_head) + + results["test_case_2"] = out.clone() + + + + return results + + + + + +# 运行测试 + +result_gold = test_triton_vs_torch() + +# print(result_gold) \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/matrix_vector_multip.py b/reflexion_oneshot_tritonbench_iter1_4/exec/matrix_vector_multip.py new file mode 100644 index 0000000..667a775 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/matrix_vector_multip.py @@ -0,0 +1,71 @@ +import torch +import triton +import triton.language as tl + +@triton.jit +def mv_kernel(A, B, C, N, M, stride_an, stride_am, stride_bm, stride_cn, BLOCK_N: tl.constexpr, BLOCK_M: tl.constexpr): + pid = tl.program_id(0) + offset_n = pid * BLOCK_N + tl.arange(0, BLOCK_N)[:, None] + offset_m = tl.arange(0, BLOCK_M)[None, :] + n_mask = offset_n < N + A_ptrs = A + offset_n * stride_an + offset_m * stride_am + B_ptrs = B + offset_m * stride_bm + acc = tl.zeros((BLOCK_N, BLOCK_M), dtype=tl.float32) + for m in range(0, M, BLOCK_M): + m_mask = m + offset_m < M + a = tl.load(A_ptrs, mask=n_mask & m_mask, other=0.0).to(tl.float32) + b = tl.load(B_ptrs, mask=m_mask, other=0.0).to(tl.float32) + acc += a * b + A_ptrs += BLOCK_M * stride_am + B_ptrs += BLOCK_M * stride_bm + acc = tl.sum(acc, axis=1) + C_ptrs = C + offset_n * stride_cn + tl.store(C_ptrs, acc[:, None], mask=n_mask) + +def mv(inp, vec): + assert inp.shape[1] == vec.shape[0], 'incompatible dimensions' + N, M = inp.shape + out = torch.empty((N,), device=inp.device, dtype=inp.dtype) + grid = lambda META: (triton.cdiv(N, META['BLOCK_N']),) + mv_kernel[grid](inp, vec, out, N, M, inp.stride(0), inp.stride(1), vec.stride(0), out.stride(0), BLOCK_N=64, BLOCK_M=64) + return out + +################################################################################################################################################## + + + + + +def test_mv(): + + # 测试用例 2: 4x3 矩阵与 3x1 向量相乘 + + A = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0], [10.0, 11.0, 12.0]], device='cuda') + + B = torch.tensor([1.0, 2.0, 3.0], device='cuda') + + triton_result_2 = mv(A, B) + + + + # 测试用例 3: 32x16 矩阵与 16x1 向量相乘 + + A = torch.randn(32, 16, device='cuda') + + B = torch.randn(16, device='cuda') + + triton_result_3 = mv(A, B) + + + + return { + + "test_case_2": triton_result_2, + + "test_case_3": triton_result_3, + + } + + + +result_gold = test_mv() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/performance_analysis.txt b/reflexion_oneshot_tritonbench_iter1_4/exec/performance_analysis.txt new file mode 100644 index 0000000..9736fc1 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/performance_analysis.txt @@ -0,0 +1,3 @@ +Performance analysis for /workspace/reflexion_oneshot_tritonbench_iter1_4/exec: +Error processing sin_kernel.json, skipping... +Error processing triton_matmul.json, skipping... \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/rotary_transform.py b/reflexion_oneshot_tritonbench_iter1_4/exec/rotary_transform.py new file mode 100644 index 0000000..4619f3b --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/rotary_transform.py @@ -0,0 +1,186 @@ +from typing import Optional, Union +import torch +import triton +import triton.language as tl + +@triton.jit +def rotary_kernel(OUT, X, COS, SIN, CU_SEQLENS, SEQLEN_OFFSETS, seqlen, nheads, rotary_dim, seqlen_ro, CACHE_KEY_SEQLEN, stride_out_batch, stride_out_seqlen, stride_out_nheads, stride_out_headdim, stride_x_batch, stride_x_seqlen, stride_x_nheads, stride_x_headdim, BLOCK_K: tl.constexpr, IS_SEQLEN_OFFSETS_TENSOR: tl.constexpr, IS_VARLEN: tl.constexpr, INTERLEAVED: tl.constexpr, CONJUGATE: tl.constexpr, BLOCK_M: tl.constexpr): + pid_m = tl.program_id(axis=0) + pid_batch = tl.program_id(axis=1) + pid_head = tl.program_id(axis=2) + rotary_dim_half = rotary_dim // 2 + if not IS_VARLEN: + X = X + pid_batch * stride_x_batch + pid_head * stride_x_nheads + OUT = OUT + pid_batch * stride_out_batch + pid_head * stride_out_nheads + else: + start_idx = tl.load(CU_SEQLENS + pid_batch) + seqlen = tl.load(CU_SEQLENS + pid_batch + 1) - start_idx + X = X + start_idx * stride_x_seqlen + pid_head * stride_x_nheads + OUT = OUT + start_idx * stride_out_seqlen + pid_head * stride_out_nheads + if pid_m * BLOCK_M >= seqlen: + return + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + if not IS_SEQLEN_OFFSETS_TENSOR: + rm_cs = rm + SEQLEN_OFFSETS + else: + rm_cs = rm + tl.load(SEQLEN_OFFSETS + pid_batch) + rk = tl.arange(0, BLOCK_K) + rk_half = tl.arange(0, BLOCK_K // 2) + if not INTERLEAVED: + X = X + (rm[:, None] * stride_x_seqlen + rk_half[None, :] * stride_x_headdim) + COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :]) + SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :]) + cos = tl.load(COS, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=1.0).to(tl.float32) + sin = tl.load(SIN, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=0.0).to(tl.float32) + x0 = tl.load(X, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), other=0.0).to(tl.float32) + x1 = tl.load(X + rotary_dim_half * stride_x_headdim, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), other=0.0).to(tl.float32) + if CONJUGATE: + sin = -sin + o0 = x0 * cos - x1 * sin + o1 = x0 * sin + x1 * cos + OUT = OUT + (rm[:, None] * stride_out_seqlen + rk_half[None, :] * stride_out_headdim) + tl.store(OUT, o0, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half)) + tl.store(OUT + rotary_dim_half * stride_out_headdim, o1, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half)) + else: + rk_swap = rk + (rk + 1) % 2 * 2 - 1 + rk_repeat = tl.arange(0, BLOCK_K) // 2 + X0 = X + (rm[:, None] * stride_x_seqlen + rk[None, :] * stride_x_headdim) + X1 = X + (rm[:, None] * stride_x_seqlen + rk_swap[None, :] * stride_x_headdim) + COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :]) + SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :]) + cos = tl.load(COS, mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half), other=1.0).to(tl.float32) + sin = tl.load(SIN, mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half), other=0.0).to(tl.float32) + x0 = tl.load(X0, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim), other=0.0).to(tl.float32) + x1 = tl.load(X1, mask=(rm[:, None] < seqlen) & (rk_swap[None, :] < rotary_dim), other=0.0).to(tl.float32) + if CONJUGATE: + sin = -sin + x0_cos = x0 * cos + x1_sin = x1 * sin + out = tl.where(rk[None, :] % 2 == 0, x0_cos - x1_sin, x0_cos + x1_sin) + OUT = OUT + (rm[:, None] * stride_out_seqlen + rk[None, :] * stride_out_headdim) + tl.store(OUT, out, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim)) + +def apply_rotary(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, seqlen_offsets: Union[int, torch.Tensor]=0, cu_seqlens: Optional[torch.Tensor]=None, max_seqlen: Optional[int]=None, interleaved=False, inplace=False, conjugate=False) -> torch.Tensor: + is_varlen = cu_seqlens is not None + if not is_varlen: + batch, seqlen, nheads, headdim = x.shape + else: + total_seqlen, nheads, headdim = x.shape + batch = cu_seqlens.shape[0] - 1 + seqlen = max_seqlen + seqlen_ro, rotary_dim = cos.shape + rotary_dim *= 2 + cos, sin = (cos.contiguous(), sin.contiguous()) + if isinstance(seqlen_offsets, torch.Tensor): + seqlen_offsets = seqlen_offsets.contiguous() + else: + seqlen_offsets += seqlen + output = torch.empty_like(x) if not inplace else x + if rotary_dim < headdim and (not inplace): + output[..., rotary_dim:].copy_(x[..., rotary_dim:]) + BLOCK_K = 32 if rotary_dim <= 32 else 64 if rotary_dim <= 64 else 128 if rotary_dim <= 128 else 256 + grid = lambda META: (triton.cdiv(seqlen, META['BLOCK_M']), batch, nheads) + BLOCK_M = 4 if interleaved else 8 if rotary_dim <= 64 else 4 + with torch.cuda.device(x.device.index): + rotary_kernel[grid](output, x, cos, sin, cu_seqlens, seqlen_offsets, seqlen, nheads, rotary_dim, seqlen_ro, seqlen // 128, output.stride(0) if not is_varlen else 0, output.stride(-3), output.stride(-2), output.stride(-1), x.stride(0) if not is_varlen else 0, x.stride(-3), x.stride(-2), x.stride(-1), BLOCK_K, isinstance(seqlen_offsets, torch.Tensor), is_varlen, interleaved, conjugate, BLOCK_M) + return output + +################################################################################################################################################## + + + + + +import torch + + + +def test_apply_rotary(): + + results = {} + + + + # Test case 1: Basic test with fixed sequence length and no interleaving + + batch, seqlen, nheads, headdim = 2, 128, 4, 64 + + rotary_dim = 32 + + x = torch.randn(batch, seqlen, nheads, headdim, device='cuda') + + cos = torch.randn(seqlen, rotary_dim // 2, device='cuda') + + sin = torch.randn(seqlen, rotary_dim // 2, device='cuda') + + output = apply_rotary(x, cos, sin) + + results['test_case_1'] = output.shape + + + + # Test case 2: Variable length sequences with interleaving + + total_seqlen, nheads, headdim = 256, 4, 64 + + batch = 3 + + cu_seqlens = torch.tensor([0, 100, 200, 256], device='cuda') + + max_seqlen = 128 + + rotary_dim = 32 + + x = torch.randn(total_seqlen, nheads, headdim, device='cuda') + + cos = torch.randn(max_seqlen, rotary_dim // 2, device='cuda') + + sin = torch.randn(max_seqlen, rotary_dim // 2, device='cuda') + + output = apply_rotary(x, cos, sin, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen, interleaved=True) + + results['test_case_2'] = output.shape + + + + # Test case 3: Conjugate flag enabled + + batch, seqlen, nheads, headdim = 2, 128, 4, 64 + + rotary_dim = 32 + + x = torch.randn(batch, seqlen, nheads, headdim, device='cuda') + + cos = torch.randn(seqlen, rotary_dim // 2, device='cuda') + + sin = torch.randn(seqlen, rotary_dim // 2, device='cuda') + + output = apply_rotary(x, cos, sin, conjugate=True) + + results['test_case_3'] = output.shape + + + + # Test case 4: Inplace operation + + batch, seqlen, nheads, headdim = 2, 128, 4, 64 + + rotary_dim = 32 + + x = torch.randn(batch, seqlen, nheads, headdim, device='cuda') + + cos = torch.randn(seqlen, rotary_dim // 2, device='cuda') + + sin = torch.randn(seqlen, rotary_dim // 2, device='cuda') + + output = apply_rotary(x, cos, sin, inplace=True) + + results['test_case_4'] = output.shape + + + + return results + + + +result_gold = test_apply_rotary() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/sin_kernel.py b/reflexion_oneshot_tritonbench_iter1_4/exec/sin_kernel.py new file mode 100644 index 0000000..d44f6b0 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/sin_kernel.py @@ -0,0 +1,86 @@ +import torch +import triton +import triton.language as tl + +@triton.jit +def kernel_function(x_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): + pid = tl.program_id(0) + block_start = pid * BLOCK_SIZE + offsets = block_start + tl.arange(0, BLOCK_SIZE) + mask = offsets < n_elements + x = tl.load(x_ptr + offsets, mask=mask) + output = tl.math.sin(x) + tl.store(output_ptr + offsets, output, mask=mask) + +def call_kernel(x): + n_elements = x.numel() + output = torch.empty_like(x) + grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),) + kernel_function[grid](x, output, n_elements, BLOCK_SIZE=1024) + return output + +################################################################################################################################################## + + + + + +import torch + + + +# Function to test the Triton kernel + +def test_call_kernel(): + + results = {} + + + + # Test case 1: Small input tensor + + x1 = torch.tensor([0.0, 1.0, 2.0, 3.0], dtype=torch.float32).cuda() + + output1 = call_kernel(x1) + + results['test_case_1'] = output1 + + + + # Test case 2: Larger input tensor + + x2 = torch.linspace(0, 10, steps=1024, dtype=torch.float32).cuda() + + output2 = call_kernel(x2) + + results['test_case_2'] = output2 + + + + # Test case 3: Edge case with zero elements + + x3 = torch.tensor([], dtype=torch.float32).cuda() + + output3 = call_kernel(x3) + + results['test_case_3'] = output3 + + + + # Test case 4: Input tensor with negative values + + x4 = torch.tensor([-1.0, -2.0, -3.0, -4.0], dtype=torch.float32).cuda() + + output4 = call_kernel(x4) + + results['test_case_4'] = output4 + + + + return results + + + +# Run the test function + +result_gold = test_call_kernel() diff --git a/reflexion_oneshot_tritonbench_iter1_4/exec/triton_matmul.py b/reflexion_oneshot_tritonbench_iter1_4/exec/triton_matmul.py new file mode 100644 index 0000000..cb96068 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/exec/triton_matmul.py @@ -0,0 +1,103 @@ +import torch +import triton +import triton.language as tl + +def _matmul_launch_metadata(grid, kernel, args): + ret = {} + M, N, K = (args['M'], args['N'], args['K']) + ret['name'] = f'{kernel.name} [M={M}, N={N}, K={K}]' + if 'c_ptr' in args: + bytes_per_elem = args['c_ptr'].element_size() + else: + bytes_per_elem = 2 + ret[f'flops{bytes_per_elem * 8}'] = 2.0 * M * N * K + ret['bytes'] = bytes_per_elem * (M * K + N * K + M * N) + return ret + +@triton.jit(launch_metadata=_matmul_launch_metadata) +def matmul_kernel(a_ptr, b_ptr, c_ptr, M, N, K, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr): + pid = tl.program_id(axis=0) + num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) + num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) + num_pid_in_group = GROUP_SIZE_M * num_pid_n + group_id = pid // num_pid_in_group + first_pid_m = group_id * GROUP_SIZE_M + group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) + pid_m = first_pid_m + pid % group_size_m + pid_n = pid % num_pid_in_group // group_size_m + start_m = pid_m * BLOCK_SIZE_M + start_n = pid_n * BLOCK_SIZE_N + offs_am = start_m + tl.arange(0, BLOCK_SIZE_M) + offs_bn = start_n + tl.arange(0, BLOCK_SIZE_N) + offs_am = tl.where(offs_am < M, offs_am, 0) + offs_bn = tl.where(offs_bn < N, offs_bn, 0) + offs_am = tl.max_contiguous(tl.multiple_of(offs_am, BLOCK_SIZE_M), BLOCK_SIZE_M) + offs_bn = tl.max_contiguous(tl.multiple_of(offs_bn, BLOCK_SIZE_N), BLOCK_SIZE_N) + offs_k = tl.arange(0, BLOCK_SIZE_K) + a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) + b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn) + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) + for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)): + a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0) + b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0) + accumulator = tl.dot(a, b, accumulator) + a_ptrs += BLOCK_SIZE_K * stride_ak + b_ptrs += BLOCK_SIZE_K * stride_bk + c = accumulator.to(c_ptr.dtype.element_ty) + offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :] + c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N) + tl.store(c_ptrs, c, mask=c_mask) + +def matmul(a, b): + configs = {torch.float16: {'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8, 'num_stages': 1, 'num_warps': 8}, torch.float32: {'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8, 'num_stages': 1, 'num_warps': 8}} + assert a.shape[1] == b.shape[0], 'Incompatible dimensions' + assert a.dtype == b.dtype, 'Incompatible dtypes' + M, K = a.shape + K, N = b.shape + dtype = a.dtype + c = torch.empty((M, N), device=a.device, dtype=dtype) + grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),) + matmul_kernel[grid](a, b, c, M, N, K, a.stride(0), a.stride(1), b.stride(0), b.stride(1), c.stride(0), c.stride(1), BLOCK_SIZE_M=configs[dtype]['BLOCK_SIZE_M'], BLOCK_SIZE_N=configs[dtype]['BLOCK_SIZE_N'], BLOCK_SIZE_K=configs[dtype]['BLOCK_SIZE_K'], GROUP_SIZE_M=configs[dtype]['GROUP_SIZE_M'], num_stages=configs[dtype]['num_stages'], num_warps=configs[dtype]['num_warps']) + return c + +################################################################################################################################################## + + + + + +import torch + + + +# Test for matmul + +def test_matmul(): + + results = {} + + M, K, N = 256, 128, 256 + + + + # Test case 1: torch.float16 + + a = torch.randn((M, K), dtype=torch.float16, device='cuda') + + b = torch.randn((K, N), dtype=torch.float16, device='cuda') + + c = matmul(a, b) + + results['test_case_1'] = c + + + + return results + + + +# Run all tests + +result_gold = test_matmul() \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/out.json b/reflexion_oneshot_tritonbench_iter1_4/out.json new file mode 100644 index 0000000..099fc75 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/out.json @@ -0,0 +1,11 @@ +2025-08-24_08-40-12 => File: matrix_vector_multip.py, Call Status: True, Exec Status: True, difficulty: -1, stderr: None +2025-08-24_08-40-32 => File: triton_matmul.py, Call Status: True, Exec Status: True, difficulty: -1, stderr: None +2025-08-24_08-40-52 => File: embedding_triton_kernel.py, Call Status: True, Exec Status: True, difficulty: -1, stderr: None +2025-08-24_08-41-35 => File: int4_matmul.py, Call Status: True, Exec Status: False, difficulty: -1, stderr: Generated output does not match reference output for file: int4_matmul.py_gen_triton_code_415247.py +2025-08-24_08-41-55 => File: flash_decode2_phi.py, Call Status: True, Exec Status: True, difficulty: -1, stderr: None +2025-08-24_08-42-15 => File: matrix_transpose.py, Call Status: True, Exec Status: True, difficulty: -1, stderr: None +2025-08-24_08-42-35 => File: rotary_transform.py, Call Status: True, Exec Status: True, difficulty: -1, stderr: None +2025-08-24_08-42-54 => File: sin_kernel.py, Call Status: True, Exec Status: True, difficulty: -1, stderr: None +2025-08-24_08-43-14 => File: l2_norm_bwd.py, Call Status: True, Exec Status: True, difficulty: -1, stderr: None +2025-08-24_08-43-34 => File: l2_norm_triton1.py, Call Status: True, Exec Status: True, difficulty: -1, stderr: None +2025-08-24_08-43-34 => File: reflexion_oneshot_tritonbench_iter1_4.json, Call Accuracy: 1.0, Exec Accuracy: 0.9 diff --git a/reflexion_oneshot_tritonbench_iter1_4/out.json_all_passes.json b/reflexion_oneshot_tritonbench_iter1_4/out.json_all_passes.json new file mode 100644 index 0000000..4bfb1f9 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/out.json_all_passes.json @@ -0,0 +1,92 @@ +[ + { + "pass_num": 0, + "file_name": "matrix_vector_multip.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "triton_matmul.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "embedding_triton_kernel.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "int4_matmul.py", + "call_status": 1, + "exec_status": 0, + "stdout": "None", + "stderr": "Generated output does not match reference output for file: int4_matmul.py_gen_triton_code_415247.py", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "flash_decode2_phi.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "matrix_transpose.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "rotary_transform.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "sin_kernel.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "l2_norm_bwd.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "l2_norm_triton1.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + } +] \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/out.json_perf_0.json b/reflexion_oneshot_tritonbench_iter1_4/out.json_perf_0.json new file mode 100644 index 0000000..aad042f --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/out.json_perf_0.json @@ -0,0 +1,20 @@ +{ + "speed_up": [ + 3.2874, + 4.8564, + 2.4925, + 2.0037, + 1.809, + 5.3753, + 0.6082 + ], + "efficiency": [ + 88.5857, + 67.5919, + 1.5125, + 1.4315, + 1.076, + 72.3781, + 13.8734 + ] +} \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/out.json_results_0.json b/reflexion_oneshot_tritonbench_iter1_4/out.json_results_0.json new file mode 100644 index 0000000..4bfb1f9 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/out.json_results_0.json @@ -0,0 +1,92 @@ +[ + { + "pass_num": 0, + "file_name": "matrix_vector_multip.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "triton_matmul.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "embedding_triton_kernel.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "int4_matmul.py", + "call_status": 1, + "exec_status": 0, + "stdout": "None", + "stderr": "Generated output does not match reference output for file: int4_matmul.py_gen_triton_code_415247.py", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "flash_decode2_phi.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "matrix_transpose.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "rotary_transform.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "sin_kernel.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "l2_norm_bwd.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + }, + { + "pass_num": 0, + "file_name": "l2_norm_triton1.py", + "call_status": 1, + "exec_status": 1, + "stdout": "None", + "stderr": "None", + "difficulty": -1 + } +] \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/out.jsonpassk.txt b/reflexion_oneshot_tritonbench_iter1_4/out.jsonpassk.txt new file mode 100644 index 0000000..46917f9 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/out.jsonpassk.txt @@ -0,0 +1,2 @@ +Call Accuracy: 100.0 +Exec Accuracy: 90.0 diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/embedding_triton_kernel.py_gen_triton_code_838485.py b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/embedding_triton_kernel.py_gen_triton_code_838485.py new file mode 100644 index 0000000..edd1eec --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/embedding_triton_kernel.py_gen_triton_code_838485.py @@ -0,0 +1,153 @@ +import torch +import triton +import triton.language as tl + +@triton.jit +def embedding_kernel(weight, input_ids, out, vob_start_id, vob_end_id, stride_weight_row, stride_out_row, n_ctx, hiden_size, BLOCK_N: tl.constexpr, BLOCK_DMODEL: tl.constexpr): + pid = tl.program_id(0) + offs_n = pid * BLOCK_N + tl.arange(0, BLOCK_N) + offs_d = tl.arange(0, BLOCK_DMODEL) + mask_n = offs_n < n_ctx + token_ids_raw = tl.load(input_ids + offs_n, mask=mask_n, other=vob_end_id) + valid_id_mask = (token_ids_raw >= vob_start_id) & (token_ids_raw < vob_end_id) + token_ids_clamped = tl.where(valid_id_mask, token_ids_raw - vob_start_id, 0) + offs_vec = token_ids_clamped[:, None] * stride_weight_row + offs_d[None, :] + load_mask = valid_id_mask[:, None] & (offs_d[None, :] < hiden_size) + vec = tl.load(weight + offs_vec, mask=load_mask, other=0.0) + vec = tl.where(valid_id_mask[:, None], vec, 0.0) + dest_offs = offs_n[:, None] * stride_out_row + offs_d[None, :] + store_mask = mask_n[:, None] & (offs_d[None, :] < hiden_size) + tl.store(out + dest_offs, vec, mask=store_mask) + +@torch.no_grad() +def embedding(input_ids: torch.Tensor, weight: torch.Tensor, vob_start_id: int, vob_end_id: int, out: torch.Tensor): + assert input_ids.ndim == 1 + assert weight.ndim == 2 + assert out.ndim == 2 and out.shape[0] == input_ids.shape[0] and (out.shape[1] == weight.shape[1]) + n_ctx = input_ids.shape[0] + BLOCK_DMODEL = triton.next_power_of_2(weight.shape[1]) + BLOCK_N = 128 + grid = (triton.cdiv(n_ctx, BLOCK_N),) + embedding_kernel[grid](weight, input_ids, out, vob_start_id, vob_end_id, weight.stride(0), out.stride(0), n_ctx, weight.shape[1], BLOCK_N=BLOCK_N, BLOCK_DMODEL=BLOCK_DMODEL, num_warps=4, num_stages=1) + +################################################################################################################################################## + + + + + +import torch + + + +def test_embedding(): + + # 参数定义 + + vocab_size = 1000 # 词汇表大小 + + embedding_dim = 512 # 嵌入维度 + + sequence_length = 128 # 输入序列长度 + + vob_start_id = 10 # 词汇表起始 ID + + vob_end_id = 1000 # 词汇表结束 ID + + + + # 创建测试输入张量 + + input_ids = torch.randint( + + vob_start_id, vob_end_id, (sequence_length,), dtype=torch.int32, device='cuda' + + ) + + weight = torch.randn( + + vocab_size, embedding_dim, dtype=torch.float32, device='cuda' + + ) + + out = torch.zeros( + + sequence_length, embedding_dim, dtype=torch.float32, device='cuda' + + ) + + + + # 调用嵌入函数 + + embedding(input_ids, weight, vob_start_id, vob_end_id, out) + + + + # 保存结果 + + results = {} + + results['test_case_1'] = out.clone() + + + + # 测试不同的输入 + + input_ids = torch.randint( + + vob_start_id, vob_end_id, (sequence_length,), dtype=torch.int32, device='cuda' + + ) + + embedding(input_ids, weight, vob_start_id, vob_end_id, out) + + results['test_case_2'] = out.clone() + + + + # 测试不同的词汇表范围 + + vob_start_id = 0 + + vob_end_id = 500 + + input_ids = torch.randint( + + vob_start_id, vob_end_id, (sequence_length,), dtype=torch.int32, device='cuda' + + ) + + embedding(input_ids, weight, vob_start_id, vob_end_id, out) + + results['test_case_3'] = out.clone() + + + + # 测试不同的嵌入维度 + + embedding_dim = 256 + + weight = torch.randn( + + vocab_size, embedding_dim, dtype=torch.float32, device='cuda' + + ) + + out = torch.zeros( + + sequence_length, embedding_dim, dtype=torch.float32, device='cuda' + + ) + + embedding(input_ids, weight, vob_start_id, vob_end_id, out) + + results['test_case_4'] = out.clone() + + + + return results + + + +result_gold = test_embedding() diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/embedding_triton_kernel.py_gen_triton_code_838485.py.stderr b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/embedding_triton_kernel.py_gen_triton_code_838485.py.stderr new file mode 100644 index 0000000..e69de29 diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/embedding_triton_kernel.py_gen_triton_code_838485.py.stdout b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/embedding_triton_kernel.py_gen_triton_code_838485.py.stdout new file mode 100644 index 0000000..10f2a81 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/embedding_triton_kernel.py_gen_triton_code_838485.py.stdout @@ -0,0 +1 @@ +True*#*#True*#*#None*#*#None \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/flash_decode2_phi.py_gen_triton_code_426066.py b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/flash_decode2_phi.py_gen_triton_code_426066.py new file mode 100644 index 0000000..3e9a502 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/flash_decode2_phi.py_gen_triton_code_426066.py @@ -0,0 +1,144 @@ +import torch +import triton +import triton.language as tl + +@triton.jit +def _fwd_kernel_flash_decode_stage2(B_Seqlen, Mid_O, Mid_O_LogExpSum, Out, stride_mid_ob, stride_mid_oh, stride_mid_os, stride_mid_od, stride_mid_o_eb, stride_mid_o_eh, stride_mid_o_es, stride_obs, stride_oh, stride_od, head_dim, BLOCK_SEQ: tl.constexpr, BLOCK_DMODEL: tl.constexpr): + cur_batch = tl.program_id(0) + cur_head = tl.program_id(1) + offs_d = tl.arange(0, BLOCK_DMODEL) + cur_batch_seq_len = tl.load(B_Seqlen + cur_batch) + block_n_size = (cur_batch_seq_len + BLOCK_SEQ - 1) // BLOCK_SEQ + sum_exp = 0.0 + max_logic = -float('inf') + acc = tl.zeros([BLOCK_DMODEL], dtype=tl.float32) + for block_seq_n in range(0, block_n_size): + tv = tl.load(Mid_O + cur_batch * stride_mid_ob + cur_head * stride_mid_oh + block_seq_n * stride_mid_os + offs_d, mask=offs_d < head_dim, other=0.0) + tlogic = tl.load(Mid_O_LogExpSum + cur_batch * stride_mid_o_eb + cur_head * stride_mid_o_eh + block_seq_n) + new_max_logic = tl.maximum(tlogic, max_logic) + old_scale = tl.exp(max_logic - new_max_logic) + acc *= old_scale + exp_logic = tl.exp(tlogic - new_max_logic) + acc += exp_logic * tv + sum_exp = sum_exp * old_scale + exp_logic + max_logic = new_max_logic + tl.store(Out + cur_batch * stride_obs + cur_head * stride_oh + offs_d, acc / sum_exp, mask=offs_d < head_dim) + +@torch.no_grad() +def flash_decode_stage2(mid_out, mid_out_logexpsum, B_Seqlen, Out, block_seq): + Lk = mid_out.shape[-1] + head_dim = Lk + batch, head_num = (mid_out.shape[0], mid_out.shape[1]) + BLOCK_DMODEL = triton.next_power_of_2(head_dim) + grid = (batch, head_num) + _fwd_kernel_flash_decode_stage2[grid](B_Seqlen, mid_out, mid_out_logexpsum, Out, mid_out.stride(0), mid_out.stride(1), mid_out.stride(2), mid_out.stride(3), mid_out_logexpsum.stride(0), mid_out_logexpsum.stride(1), mid_out_logexpsum.stride(2), Out.stride(0), Out.stride(1), Out.stride(2), head_dim, BLOCK_SEQ=block_seq, BLOCK_DMODEL=BLOCK_DMODEL, num_warps=4, num_stages=2) + +################################################################################################################################################## + + + + + +import torch + + + +# Define the test function + +def test_flash_decode_stage2(): + + # Define the parameters for different test cases + + batch_size = 2 + + head_num = 4 + + seq_block_num = 3 + + head_dim = 64 + + block_seq = 16 + + + + test_cases = { + + "test_case_1": { + + "B_Seqlen": torch.randint(1, seq_block_num * block_seq, (batch_size,), dtype=torch.int32, device='cuda'), + + "mid_out": torch.randn((batch_size, head_num, seq_block_num, head_dim), dtype=torch.float32, device='cuda'), + + "mid_out_logexpsum": torch.randn((batch_size, head_num, seq_block_num), dtype=torch.float32, device='cuda'), + + "Out": torch.zeros((batch_size, head_num, head_dim), dtype=torch.float32, device='cuda'), + + "block_seq": block_seq + + }, + + "test_case_2": { + + "B_Seqlen": torch.randint(1, seq_block_num * block_seq, (batch_size,), dtype=torch.int32, device='cuda'), + + "mid_out": torch.randn((batch_size, head_num, seq_block_num, head_dim), dtype=torch.float32, device='cuda'), + + "mid_out_logexpsum": torch.randn((batch_size, head_num, seq_block_num), dtype=torch.float32, device='cuda'), + + "Out": torch.zeros((batch_size, head_num, head_dim), dtype=torch.float32, device='cuda'), + + "block_seq": block_seq + 1 # Different block size + + }, + + "test_case_3": { + + "B_Seqlen": torch.randint(1, seq_block_num * block_seq, (batch_size,), dtype=torch.int32, device='cuda'), + + "mid_out": torch.randn((batch_size, head_num, seq_block_num, head_dim), dtype=torch.float32, device='cuda'), + + "mid_out_logexpsum": torch.randn((batch_size, head_num, seq_block_num), dtype=torch.float32, device='cuda'), + + "Out": torch.zeros((batch_size, head_num, head_dim), dtype=torch.float32, device='cuda'), + + "block_seq": block_seq // 2 # Different block size + + }, + + "test_case_4": { + + "B_Seqlen": torch.randint(1, seq_block_num * block_seq, (batch_size,), dtype=torch.int32, device='cuda'), + + "mid_out": torch.randn((batch_size, head_num, seq_block_num, head_dim), dtype=torch.float32, device='cuda'), + + "mid_out_logexpsum": torch.randn((batch_size, head_num, seq_block_num), dtype=torch.float32, device='cuda'), + + "Out": torch.zeros((batch_size, head_num, head_dim), dtype=torch.float32, device='cuda'), + + "block_seq": block_seq * 2 # Different block size + + } + + } + + + + # Execute the function for all test cases + + results = {} + + for key, test_case in test_cases.items(): + + flash_decode_stage2(test_case["mid_out"], test_case["mid_out_logexpsum"], test_case["B_Seqlen"], test_case["Out"], test_case["block_seq"]) + + results[key] = test_case["Out"] + + + + return results + + + +# Run the test + +result_gold = test_flash_decode_stage2() diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/flash_decode2_phi.py_gen_triton_code_426066.py.stderr b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/flash_decode2_phi.py_gen_triton_code_426066.py.stderr new file mode 100644 index 0000000..e69de29 diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/flash_decode2_phi.py_gen_triton_code_426066.py.stdout b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/flash_decode2_phi.py_gen_triton_code_426066.py.stdout new file mode 100644 index 0000000..10f2a81 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/flash_decode2_phi.py_gen_triton_code_426066.py.stdout @@ -0,0 +1 @@ +True*#*#True*#*#None*#*#None \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/int4_matmul.py_gen_triton_code_415247.py b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/int4_matmul.py_gen_triton_code_415247.py new file mode 100644 index 0000000..6e3d025 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/int4_matmul.py_gen_triton_code_415247.py @@ -0,0 +1,142 @@ +import torch +import triton +import triton.language as tl + +@triton.autotune(configs=[triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4), triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4), triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4)], key=['M', 'N', 'K'], reset_to_zero=['c_ptr']) +@triton.jit +def matmul_kernel(a_ptr, b_ptr, c_ptr, bs_ptr, bzp_ptr, M, N, K, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, stride_bsk, stride_bsn, stride_bzpk, stride_bzpn, group_size, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr, SPLIT_K: tl.constexpr): + pid = tl.program_id(axis=0) + pid_sp_k = tl.program_id(axis=1) + num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) + num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) + num_pid_k = tl.cdiv(K, BLOCK_SIZE_K) + num_pid_in_group = GROUP_SIZE_M * num_pid_n + group_id = pid // num_pid_in_group + first_pid_m = group_id * GROUP_SIZE_M + group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) + pid_m = first_pid_m + pid % group_size_m + pid_n = pid % num_pid_in_group // group_size_m + offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M + offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N + offs_k = pid_sp_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K) + a_ptrs = a_ptr + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak + b_ptrs = b_ptr + offs_k[:, None] // 8 * stride_bk + offs_bn[None, :] * stride_bn + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) + for k in range(0, tl.cdiv(K, BLOCK_SIZE_K * SPLIT_K)): + bs_ptrs = bs_ptr + (offs_k[:, None] + k * BLOCK_SIZE_K * SPLIT_K) // group_size * stride_bsk + offs_bn[None, :] * stride_bsn + bzp_ptrs = bzp_ptr + (offs_k[:, None] + k * BLOCK_SIZE_K * SPLIT_K) // group_size * stride_bzpk + offs_bn[None, :] // 8 * stride_bzpn + b_shift_bits = offs_k[:, None] % 8 * 4 + bzp_shift_bits = offs_bn[None, :] % 8 * 4 + a = tl.load(a_ptrs) + b = tl.load(b_ptrs) + bs = tl.load(bs_ptrs) + bzp = tl.load(bzp_ptrs) + int_b = b >> b_shift_bits & 15 + int_bzp = bzp >> bzp_shift_bits & 15 + b = ((int_b - int_bzp) * bs).to(a.dtype) + accumulator += tl.dot(a, b) + a_ptrs += BLOCK_SIZE_K * SPLIT_K * stride_ak + b_ptrs += BLOCK_SIZE_K * SPLIT_K * stride_bk // 8 + c = accumulator.to(c_ptr.dtype.element_ty) + offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :] + c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N) + if SPLIT_K == 1: + tl.store(c_ptrs, c, mask=c_mask) + else: + tl.atomic_add(c_ptrs, c, mask=c_mask) + +def matmul_dequantize_int4_s2(x: torch.FloatTensor, qweight: torch.IntTensor, scales: torch.FloatTensor, qzeros: torch.IntTensor, group_size: int=128, output=None) -> torch.FloatTensor: + assert x.is_contiguous(), 'A must be contiguous' + assert qweight.is_contiguous(), 'B must be contiguous' + M, K = x.shape + N = scales.shape[1] + if output is None: + output = torch.zeros((M, N), device=x.device, dtype=x.dtype) + grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), META['SPLIT_K']) + matmul_kernel[grid](x, qweight, output, scales, qzeros, M, N, K, x.stride(0), x.stride(1), qweight.stride(0), qweight.stride(1), output.stride(0), output.stride(1), scales.stride(0), scales.stride(1), qzeros.stride(0), qzeros.stride(1), group_size) + return output + +def quantize_int4(weight, group_size=128, tp_rank=0): + weight = weight.transpose(1, 0) + h1, h2 = weight.shape + assert h1 % 8 == 0 and h2 % 8 == 0, 'H1 {} H2 {}'.format(h1, h2) + assert h2 % group_size == 0, 'H1 {} H2 {}'.format(h1, h2) + weight = weight.contiguous().view(-1, group_size).cuda(tp_rank) + weight_max = weight.amax(-1, keepdim=True) + weight_max = torch.where(weight_max < 0, 0, weight_max) + weight_min = weight.amin(-1, keepdim=True) + weight_min = torch.where(weight_min > 0, 0, weight_min) + weight_range = weight_max - weight_min + scale = weight_range / (2 ** 4 - 1) + zero_point = (-weight_min / scale).round().clamp(0, 15).to(torch.int32) + weight = (weight / scale + zero_point).round().clamp(0, 15).to(torch.int32).view(h1, h2) + int_weight = torch.empty(h1, h2 // 8, dtype=torch.int32, device=weight.device) + int_zero_point = torch.zeros(h1 // 8, h2 // group_size, dtype=torch.int32, device=weight.device) + zero_point = zero_point.view(h1, -1) + scale = scale.view(h1, -1) + for pack in range(0, h2, 8): + for i in range(8): + int_weight[:, pack // 8] += weight[:, pack + i] << i * 4 + for pack in range(0, h1, 8): + for i in range(8): + int_zero_point[pack // 8, :] += zero_point[pack + i, :] << i * 4 + return (int_weight.transpose(1, 0).contiguous(), scale.transpose(1, 0).contiguous(), int_zero_point.transpose(1, 0).contiguous(), group_size) + +def unpack_int4(weight, scale, zp): + weight = weight.transpose(1, 0) + scale = scale.transpose(1, 0) + zp = zp.transpose(1, 0) + h1, h2 = weight.shape + group_size = h2 * 8 // scale.shape[1] + group_num = scale.shape[1] + fp_weight = torch.zeros(h1, h2 * 8, dtype=torch.half, device=weight.device) + fp_zero_point = torch.zeros(h1, group_num, device=weight.device) + for pack in range(0, h2): + for i in range(8): + fp_weight[:, pack * 8 + i] = weight[:, pack] >> i * 4 & 15 + for pack in range(0, h1 // 8): + for i in range(8): + fp_zero_point[pack * 8 + i, :] = zp[pack, :] >> i * 4 & 15 + for g in range(group_num): + fp_weight[:, g * group_size:(g + 1) * group_size] = (fp_weight[:, g * group_size:(g + 1) * group_size] - fp_zero_point[:, g].unsqueeze(1)) * scale[:, g].unsqueeze(1) + return fp_weight.transpose(1, 0) + +################################################################################################################################################## + + + + + +def test_correct_int4_s2(M=32, K=4096, N=4096): + + group_size = 128 + + a = torch.randn((M, K), device='cuda', dtype=torch.float16) + + b = torch.randn((K, N), device='cuda', dtype=torch.float16) + + int_b, b_scale, b_zero_point, _ = quantize_int4(b, group_size=group_size) + + + + # Test case + + triton_output = matmul_dequantize_int4_s2(a, int_b, b_scale, b_zero_point, group_size) + + + + results = { + + "test_case_1": triton_output + + } + + + + return results + + + +result_gold = test_correct_int4_s2() diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/int4_matmul.py_gen_triton_code_415247.py.stderr b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/int4_matmul.py_gen_triton_code_415247.py.stderr new file mode 100644 index 0000000..e69de29 diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/int4_matmul.py_gen_triton_code_415247.py.stdout b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/int4_matmul.py_gen_triton_code_415247.py.stdout new file mode 100644 index 0000000..c5cd3a9 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/int4_matmul.py_gen_triton_code_415247.py.stdout @@ -0,0 +1 @@ +True*#*#False*#*#None*#*#Generated output does not match reference output for file: int4_matmul.py_gen_triton_code_415247.py \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_bwd.py_gen_triton_code_284499.py b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_bwd.py_gen_triton_code_284499.py new file mode 100644 index 0000000..0608484 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_bwd.py_gen_triton_code_284499.py @@ -0,0 +1,108 @@ +import torch +import triton +import triton.language as tl + +@triton.jit +def _l2_norm_bwd_kernel(X, DY, DX, stride_x_row, N, eps, BLOCK_N: tl.constexpr): + row = tl.program_id(0) + X += row * stride_x_row + DX += row * stride_x_row + DY += row * stride_x_row + cols = tl.arange(0, BLOCK_N) + mask = cols < N + x = tl.load(X + cols, mask=mask, other=0.0).to(tl.float32) + dy = tl.load(DY + cols, mask=mask, other=0.0).to(tl.float32) + var = tl.sum(x * x) + rstd = 1 / tl.math.sqrt(var + eps) + dx = dy * rstd - tl.sum(dy * x) * (1 / (var + eps)) * rstd * x + tl.store(DX + cols, dx, mask=mask) + +def _l2_norm_bwd(x: torch.Tensor, dy: torch.Tensor, eps: float=1e-05): + x_shape_og = x.shape + x = x.reshape(-1, dy.shape[-1]) + dy = dy.reshape(-1, dy.shape[-1]) + if dy.stride(-1) != 1: + dy = dy.contiguous() + dx = torch.empty_like(x) + N = x.shape[-1] + M = x.shape[0] + MAX_FUSED_SIZE = 65536 // x.element_size() + BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N)) + if N > BLOCK_N: + raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.") + _l2_norm_bwd_kernel[M,](x, dy, dx, x.stride(0), N, eps, BLOCK_N=BLOCK_N) + return dx.reshape(x_shape_og) + +################################################################################################################################################## + + + + + +import torch + + + +# Test the backward L2 normalization + +def test_l2_norm_bwd(): + + results = {} + + + + # Test case 1: Default case + + x = torch.randn(4, 8, device='cuda', dtype=torch.float32) + + dy = torch.randn(4, 8, device='cuda', dtype=torch.float32) + + dx = _l2_norm_bwd(x, dy) + + results['test_case_1'] = dx + + + + # Test case 2: Different shape + + x = torch.randn(2, 16, device='cuda', dtype=torch.float32) + + dy = torch.randn(2, 16, device='cuda', dtype=torch.float32) + + dx = _l2_norm_bwd(x, dy) + + results['test_case_2'] = dx + + + + # Test case 3: Larger tensor + + x = torch.randn(8, 8, device='cuda', dtype=torch.float32) + + dy = torch.randn(8, 8, device='cuda', dtype=torch.float32) + + dx = _l2_norm_bwd(x, dy) + + results['test_case_3'] = dx + + + + # Test case 4: Edge case with small tensor + + x = torch.randn(1, 8, device='cuda', dtype=torch.float32) + + dy = torch.randn(1, 8, device='cuda', dtype=torch.float32) + + dx = _l2_norm_bwd(x, dy) + + results['test_case_4'] = dx + + + + return results + + + +# Run the tests + +result_gold = test_l2_norm_bwd() diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_bwd.py_gen_triton_code_284499.py.stderr b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_bwd.py_gen_triton_code_284499.py.stderr new file mode 100644 index 0000000..e69de29 diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_bwd.py_gen_triton_code_284499.py.stdout b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_bwd.py_gen_triton_code_284499.py.stdout new file mode 100644 index 0000000..10f2a81 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_bwd.py_gen_triton_code_284499.py.stdout @@ -0,0 +1 @@ +True*#*#True*#*#None*#*#None \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_triton1.py_gen_triton_code_572781.py b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_triton1.py_gen_triton_code_572781.py new file mode 100644 index 0000000..d772e24 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_triton1.py_gen_triton_code_572781.py @@ -0,0 +1,96 @@ +import torch +import triton +import triton.language as tl + +@triton.jit +def _l2_norm_fwd_1pass_kernel(X, Y, stride_x_row, N, eps, BLOCK_N: tl.constexpr): + row = tl.program_id(0) + X += row * stride_x_row + Y += row * stride_x_row + cols = tl.arange(0, BLOCK_N) + mask = cols < N + x = tl.load(X + cols, mask=mask, other=0.0).to(tl.float32) + var = tl.sum(x * x, axis=0) + rstd = 1 / tl.sqrt(var + eps) + y = x * rstd + tl.store(Y + cols, y, mask=mask) + +def _l2_norm_fwd(x, eps=1e-06): + x_shape_og = x.shape + x = x.reshape(-1, x.shape[-1]) + if x.stride(-1) != 1: + x = x.contiguous() + y = torch.empty_like(x) + N = x.shape[-1] + M = x.shape[0] + MAX_FUSED_SIZE = 65536 // x.element_size() + BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N)) + if N > BLOCK_N: + raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.") + grid = (M,) + _l2_norm_fwd_1pass_kernel[grid](x, y, stride_x_row=x.stride(0), N=N, eps=eps, BLOCK_N=BLOCK_N) + return y.reshape(x_shape_og) + +################################################################################################################################################## + + + + + +import torch + + + +# Test the forward L2 normalization + +def test_l2_norm_fwd(): + + results = {} + + + + # Test case 1 + + x1 = torch.randn(4, 8, device='cuda', dtype=torch.float32) + + y1 = _l2_norm_fwd(x1) + + results['test_case_1'] = y1 + + + + # Test case 2: Different batch size + + x2 = torch.randn(2, 8, device='cuda', dtype=torch.float32) + + y2 = _l2_norm_fwd(x2) + + results['test_case_2'] = y2 + + + + # Test case 3: Different feature size + + x3 = torch.randn(4, 4, device='cuda', dtype=torch.float32) + + y3 = _l2_norm_fwd(x3) + + results['test_case_3'] = y3 + + + + # Test case 4: Larger tensor + + x4 = torch.randn(8, 8, device='cuda', dtype=torch.float32) + + y4 = _l2_norm_fwd(x4) + + results['test_case_4'] = y4 + + + + return results + + + +result_gold = test_l2_norm_fwd() diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_triton1.py_gen_triton_code_572781.py.stderr b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_triton1.py_gen_triton_code_572781.py.stderr new file mode 100644 index 0000000..e69de29 diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_triton1.py_gen_triton_code_572781.py.stdout b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_triton1.py_gen_triton_code_572781.py.stdout new file mode 100644 index 0000000..10f2a81 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/l2_norm_triton1.py_gen_triton_code_572781.py.stdout @@ -0,0 +1 @@ +True*#*#True*#*#None*#*#None \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_transpose.py_gen_triton_code_602176.py b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_transpose.py_gen_triton_code_602176.py new file mode 100644 index 0000000..1f125a0 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_transpose.py_gen_triton_code_602176.py @@ -0,0 +1,74 @@ +import torch +import triton +import triton.language as tl + +@triton.jit +def kernel(M, Out, matrix_stridex, matrix_stridey, out_stridex, out_stridey, SIZE_M, D_HEAD, BLOCK_M: tl.constexpr, BLOCK_D: tl.constexpr): + pid_m = tl.program_id(0) + pid_d = tl.program_id(1) + offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + offs_d = pid_d * BLOCK_D + tl.arange(0, BLOCK_D) + mask_m = offs_m < SIZE_M + mask_d = offs_d < D_HEAD + inp_ptrs = M + offs_m[:, None] * matrix_stridex + offs_d[None, :] * matrix_stridey + out_ptrs = Out + offs_d[:, None] * out_stridex + offs_m[None, :] * out_stridey + tile = tl.load(inp_ptrs, mask=mask_m[:, None] & mask_d[None, :], other=0.0) + tl.store(out_ptrs, tile.T, mask=mask_d[:, None] & mask_m[None, :]) + +def wrapper(size_m: int, d_head: int) -> torch.Tensor: + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + matrix = torch.randn((size_m, d_head), dtype=torch.float16, device=device) + out = torch.empty((d_head, size_m), dtype=torch.float16, device=device) + BLOCK_M = 16 + BLOCK_D = 16 + grid = (triton.cdiv(size_m, BLOCK_M), triton.cdiv(d_head, BLOCK_D)) + kernel[grid](matrix, out, matrix.stride(0), matrix.stride(1), out.stride(0), out.stride(1), size_m, d_head, BLOCK_M=BLOCK_M, BLOCK_D=BLOCK_D) + return out + +################################################################################################################################################## + + + + + +import torch + + + +def test_triton_vs_torch(): + + results = {} + + + + # 测试用例 1: 基本矩阵转置 (小矩阵) + + size_m, d_head = 16, 16 + + out = wrapper(size_m, d_head) + + results["test_case_1"] = out.clone() + + + + # 测试用例 2: 非方形矩阵 + + size_m, d_head = 32, 64 + + out = wrapper(size_m, d_head) + + results["test_case_2"] = out.clone() + + + + return results + + + + + +# 运行测试 + +result_gold = test_triton_vs_torch() + +# print(result_gold) \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_transpose.py_gen_triton_code_602176.py.stderr b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_transpose.py_gen_triton_code_602176.py.stderr new file mode 100644 index 0000000..e69de29 diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_transpose.py_gen_triton_code_602176.py.stdout b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_transpose.py_gen_triton_code_602176.py.stdout new file mode 100644 index 0000000..10f2a81 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_transpose.py_gen_triton_code_602176.py.stdout @@ -0,0 +1 @@ +True*#*#True*#*#None*#*#None \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_vector_multip.py_gen_triton_code_220714.py b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_vector_multip.py_gen_triton_code_220714.py new file mode 100644 index 0000000..667a775 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_vector_multip.py_gen_triton_code_220714.py @@ -0,0 +1,71 @@ +import torch +import triton +import triton.language as tl + +@triton.jit +def mv_kernel(A, B, C, N, M, stride_an, stride_am, stride_bm, stride_cn, BLOCK_N: tl.constexpr, BLOCK_M: tl.constexpr): + pid = tl.program_id(0) + offset_n = pid * BLOCK_N + tl.arange(0, BLOCK_N)[:, None] + offset_m = tl.arange(0, BLOCK_M)[None, :] + n_mask = offset_n < N + A_ptrs = A + offset_n * stride_an + offset_m * stride_am + B_ptrs = B + offset_m * stride_bm + acc = tl.zeros((BLOCK_N, BLOCK_M), dtype=tl.float32) + for m in range(0, M, BLOCK_M): + m_mask = m + offset_m < M + a = tl.load(A_ptrs, mask=n_mask & m_mask, other=0.0).to(tl.float32) + b = tl.load(B_ptrs, mask=m_mask, other=0.0).to(tl.float32) + acc += a * b + A_ptrs += BLOCK_M * stride_am + B_ptrs += BLOCK_M * stride_bm + acc = tl.sum(acc, axis=1) + C_ptrs = C + offset_n * stride_cn + tl.store(C_ptrs, acc[:, None], mask=n_mask) + +def mv(inp, vec): + assert inp.shape[1] == vec.shape[0], 'incompatible dimensions' + N, M = inp.shape + out = torch.empty((N,), device=inp.device, dtype=inp.dtype) + grid = lambda META: (triton.cdiv(N, META['BLOCK_N']),) + mv_kernel[grid](inp, vec, out, N, M, inp.stride(0), inp.stride(1), vec.stride(0), out.stride(0), BLOCK_N=64, BLOCK_M=64) + return out + +################################################################################################################################################## + + + + + +def test_mv(): + + # 测试用例 2: 4x3 矩阵与 3x1 向量相乘 + + A = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0], [10.0, 11.0, 12.0]], device='cuda') + + B = torch.tensor([1.0, 2.0, 3.0], device='cuda') + + triton_result_2 = mv(A, B) + + + + # 测试用例 3: 32x16 矩阵与 16x1 向量相乘 + + A = torch.randn(32, 16, device='cuda') + + B = torch.randn(16, device='cuda') + + triton_result_3 = mv(A, B) + + + + return { + + "test_case_2": triton_result_2, + + "test_case_3": triton_result_3, + + } + + + +result_gold = test_mv() diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_vector_multip.py_gen_triton_code_220714.py.stderr b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_vector_multip.py_gen_triton_code_220714.py.stderr new file mode 100644 index 0000000..e69de29 diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_vector_multip.py_gen_triton_code_220714.py.stdout b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_vector_multip.py_gen_triton_code_220714.py.stdout new file mode 100644 index 0000000..10f2a81 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/matrix_vector_multip.py_gen_triton_code_220714.py.stdout @@ -0,0 +1 @@ +True*#*#True*#*#None*#*#None \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/rotary_transform.py_gen_triton_code_249792.py b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/rotary_transform.py_gen_triton_code_249792.py new file mode 100644 index 0000000..4619f3b --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/rotary_transform.py_gen_triton_code_249792.py @@ -0,0 +1,186 @@ +from typing import Optional, Union +import torch +import triton +import triton.language as tl + +@triton.jit +def rotary_kernel(OUT, X, COS, SIN, CU_SEQLENS, SEQLEN_OFFSETS, seqlen, nheads, rotary_dim, seqlen_ro, CACHE_KEY_SEQLEN, stride_out_batch, stride_out_seqlen, stride_out_nheads, stride_out_headdim, stride_x_batch, stride_x_seqlen, stride_x_nheads, stride_x_headdim, BLOCK_K: tl.constexpr, IS_SEQLEN_OFFSETS_TENSOR: tl.constexpr, IS_VARLEN: tl.constexpr, INTERLEAVED: tl.constexpr, CONJUGATE: tl.constexpr, BLOCK_M: tl.constexpr): + pid_m = tl.program_id(axis=0) + pid_batch = tl.program_id(axis=1) + pid_head = tl.program_id(axis=2) + rotary_dim_half = rotary_dim // 2 + if not IS_VARLEN: + X = X + pid_batch * stride_x_batch + pid_head * stride_x_nheads + OUT = OUT + pid_batch * stride_out_batch + pid_head * stride_out_nheads + else: + start_idx = tl.load(CU_SEQLENS + pid_batch) + seqlen = tl.load(CU_SEQLENS + pid_batch + 1) - start_idx + X = X + start_idx * stride_x_seqlen + pid_head * stride_x_nheads + OUT = OUT + start_idx * stride_out_seqlen + pid_head * stride_out_nheads + if pid_m * BLOCK_M >= seqlen: + return + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + if not IS_SEQLEN_OFFSETS_TENSOR: + rm_cs = rm + SEQLEN_OFFSETS + else: + rm_cs = rm + tl.load(SEQLEN_OFFSETS + pid_batch) + rk = tl.arange(0, BLOCK_K) + rk_half = tl.arange(0, BLOCK_K // 2) + if not INTERLEAVED: + X = X + (rm[:, None] * stride_x_seqlen + rk_half[None, :] * stride_x_headdim) + COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :]) + SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :]) + cos = tl.load(COS, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=1.0).to(tl.float32) + sin = tl.load(SIN, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=0.0).to(tl.float32) + x0 = tl.load(X, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), other=0.0).to(tl.float32) + x1 = tl.load(X + rotary_dim_half * stride_x_headdim, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), other=0.0).to(tl.float32) + if CONJUGATE: + sin = -sin + o0 = x0 * cos - x1 * sin + o1 = x0 * sin + x1 * cos + OUT = OUT + (rm[:, None] * stride_out_seqlen + rk_half[None, :] * stride_out_headdim) + tl.store(OUT, o0, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half)) + tl.store(OUT + rotary_dim_half * stride_out_headdim, o1, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half)) + else: + rk_swap = rk + (rk + 1) % 2 * 2 - 1 + rk_repeat = tl.arange(0, BLOCK_K) // 2 + X0 = X + (rm[:, None] * stride_x_seqlen + rk[None, :] * stride_x_headdim) + X1 = X + (rm[:, None] * stride_x_seqlen + rk_swap[None, :] * stride_x_headdim) + COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :]) + SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :]) + cos = tl.load(COS, mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half), other=1.0).to(tl.float32) + sin = tl.load(SIN, mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half), other=0.0).to(tl.float32) + x0 = tl.load(X0, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim), other=0.0).to(tl.float32) + x1 = tl.load(X1, mask=(rm[:, None] < seqlen) & (rk_swap[None, :] < rotary_dim), other=0.0).to(tl.float32) + if CONJUGATE: + sin = -sin + x0_cos = x0 * cos + x1_sin = x1 * sin + out = tl.where(rk[None, :] % 2 == 0, x0_cos - x1_sin, x0_cos + x1_sin) + OUT = OUT + (rm[:, None] * stride_out_seqlen + rk[None, :] * stride_out_headdim) + tl.store(OUT, out, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim)) + +def apply_rotary(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, seqlen_offsets: Union[int, torch.Tensor]=0, cu_seqlens: Optional[torch.Tensor]=None, max_seqlen: Optional[int]=None, interleaved=False, inplace=False, conjugate=False) -> torch.Tensor: + is_varlen = cu_seqlens is not None + if not is_varlen: + batch, seqlen, nheads, headdim = x.shape + else: + total_seqlen, nheads, headdim = x.shape + batch = cu_seqlens.shape[0] - 1 + seqlen = max_seqlen + seqlen_ro, rotary_dim = cos.shape + rotary_dim *= 2 + cos, sin = (cos.contiguous(), sin.contiguous()) + if isinstance(seqlen_offsets, torch.Tensor): + seqlen_offsets = seqlen_offsets.contiguous() + else: + seqlen_offsets += seqlen + output = torch.empty_like(x) if not inplace else x + if rotary_dim < headdim and (not inplace): + output[..., rotary_dim:].copy_(x[..., rotary_dim:]) + BLOCK_K = 32 if rotary_dim <= 32 else 64 if rotary_dim <= 64 else 128 if rotary_dim <= 128 else 256 + grid = lambda META: (triton.cdiv(seqlen, META['BLOCK_M']), batch, nheads) + BLOCK_M = 4 if interleaved else 8 if rotary_dim <= 64 else 4 + with torch.cuda.device(x.device.index): + rotary_kernel[grid](output, x, cos, sin, cu_seqlens, seqlen_offsets, seqlen, nheads, rotary_dim, seqlen_ro, seqlen // 128, output.stride(0) if not is_varlen else 0, output.stride(-3), output.stride(-2), output.stride(-1), x.stride(0) if not is_varlen else 0, x.stride(-3), x.stride(-2), x.stride(-1), BLOCK_K, isinstance(seqlen_offsets, torch.Tensor), is_varlen, interleaved, conjugate, BLOCK_M) + return output + +################################################################################################################################################## + + + + + +import torch + + + +def test_apply_rotary(): + + results = {} + + + + # Test case 1: Basic test with fixed sequence length and no interleaving + + batch, seqlen, nheads, headdim = 2, 128, 4, 64 + + rotary_dim = 32 + + x = torch.randn(batch, seqlen, nheads, headdim, device='cuda') + + cos = torch.randn(seqlen, rotary_dim // 2, device='cuda') + + sin = torch.randn(seqlen, rotary_dim // 2, device='cuda') + + output = apply_rotary(x, cos, sin) + + results['test_case_1'] = output.shape + + + + # Test case 2: Variable length sequences with interleaving + + total_seqlen, nheads, headdim = 256, 4, 64 + + batch = 3 + + cu_seqlens = torch.tensor([0, 100, 200, 256], device='cuda') + + max_seqlen = 128 + + rotary_dim = 32 + + x = torch.randn(total_seqlen, nheads, headdim, device='cuda') + + cos = torch.randn(max_seqlen, rotary_dim // 2, device='cuda') + + sin = torch.randn(max_seqlen, rotary_dim // 2, device='cuda') + + output = apply_rotary(x, cos, sin, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen, interleaved=True) + + results['test_case_2'] = output.shape + + + + # Test case 3: Conjugate flag enabled + + batch, seqlen, nheads, headdim = 2, 128, 4, 64 + + rotary_dim = 32 + + x = torch.randn(batch, seqlen, nheads, headdim, device='cuda') + + cos = torch.randn(seqlen, rotary_dim // 2, device='cuda') + + sin = torch.randn(seqlen, rotary_dim // 2, device='cuda') + + output = apply_rotary(x, cos, sin, conjugate=True) + + results['test_case_3'] = output.shape + + + + # Test case 4: Inplace operation + + batch, seqlen, nheads, headdim = 2, 128, 4, 64 + + rotary_dim = 32 + + x = torch.randn(batch, seqlen, nheads, headdim, device='cuda') + + cos = torch.randn(seqlen, rotary_dim // 2, device='cuda') + + sin = torch.randn(seqlen, rotary_dim // 2, device='cuda') + + output = apply_rotary(x, cos, sin, inplace=True) + + results['test_case_4'] = output.shape + + + + return results + + + +result_gold = test_apply_rotary() diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/rotary_transform.py_gen_triton_code_249792.py.stderr b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/rotary_transform.py_gen_triton_code_249792.py.stderr new file mode 100644 index 0000000..e69de29 diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/rotary_transform.py_gen_triton_code_249792.py.stdout b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/rotary_transform.py_gen_triton_code_249792.py.stdout new file mode 100644 index 0000000..10f2a81 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/rotary_transform.py_gen_triton_code_249792.py.stdout @@ -0,0 +1 @@ +True*#*#True*#*#None*#*#None \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/sin_kernel.py_gen_triton_code_687736.py b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/sin_kernel.py_gen_triton_code_687736.py new file mode 100644 index 0000000..d44f6b0 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/sin_kernel.py_gen_triton_code_687736.py @@ -0,0 +1,86 @@ +import torch +import triton +import triton.language as tl + +@triton.jit +def kernel_function(x_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): + pid = tl.program_id(0) + block_start = pid * BLOCK_SIZE + offsets = block_start + tl.arange(0, BLOCK_SIZE) + mask = offsets < n_elements + x = tl.load(x_ptr + offsets, mask=mask) + output = tl.math.sin(x) + tl.store(output_ptr + offsets, output, mask=mask) + +def call_kernel(x): + n_elements = x.numel() + output = torch.empty_like(x) + grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),) + kernel_function[grid](x, output, n_elements, BLOCK_SIZE=1024) + return output + +################################################################################################################################################## + + + + + +import torch + + + +# Function to test the Triton kernel + +def test_call_kernel(): + + results = {} + + + + # Test case 1: Small input tensor + + x1 = torch.tensor([0.0, 1.0, 2.0, 3.0], dtype=torch.float32).cuda() + + output1 = call_kernel(x1) + + results['test_case_1'] = output1 + + + + # Test case 2: Larger input tensor + + x2 = torch.linspace(0, 10, steps=1024, dtype=torch.float32).cuda() + + output2 = call_kernel(x2) + + results['test_case_2'] = output2 + + + + # Test case 3: Edge case with zero elements + + x3 = torch.tensor([], dtype=torch.float32).cuda() + + output3 = call_kernel(x3) + + results['test_case_3'] = output3 + + + + # Test case 4: Input tensor with negative values + + x4 = torch.tensor([-1.0, -2.0, -3.0, -4.0], dtype=torch.float32).cuda() + + output4 = call_kernel(x4) + + results['test_case_4'] = output4 + + + + return results + + + +# Run the test function + +result_gold = test_call_kernel() diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/sin_kernel.py_gen_triton_code_687736.py.stderr b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/sin_kernel.py_gen_triton_code_687736.py.stderr new file mode 100644 index 0000000..e69de29 diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/sin_kernel.py_gen_triton_code_687736.py.stdout b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/sin_kernel.py_gen_triton_code_687736.py.stdout new file mode 100644 index 0000000..10f2a81 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/sin_kernel.py_gen_triton_code_687736.py.stdout @@ -0,0 +1 @@ +True*#*#True*#*#None*#*#None \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/triton_matmul.py_gen_triton_code_949726.py b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/triton_matmul.py_gen_triton_code_949726.py new file mode 100644 index 0000000..cb96068 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/triton_matmul.py_gen_triton_code_949726.py @@ -0,0 +1,103 @@ +import torch +import triton +import triton.language as tl + +def _matmul_launch_metadata(grid, kernel, args): + ret = {} + M, N, K = (args['M'], args['N'], args['K']) + ret['name'] = f'{kernel.name} [M={M}, N={N}, K={K}]' + if 'c_ptr' in args: + bytes_per_elem = args['c_ptr'].element_size() + else: + bytes_per_elem = 2 + ret[f'flops{bytes_per_elem * 8}'] = 2.0 * M * N * K + ret['bytes'] = bytes_per_elem * (M * K + N * K + M * N) + return ret + +@triton.jit(launch_metadata=_matmul_launch_metadata) +def matmul_kernel(a_ptr, b_ptr, c_ptr, M, N, K, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr): + pid = tl.program_id(axis=0) + num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) + num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) + num_pid_in_group = GROUP_SIZE_M * num_pid_n + group_id = pid // num_pid_in_group + first_pid_m = group_id * GROUP_SIZE_M + group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) + pid_m = first_pid_m + pid % group_size_m + pid_n = pid % num_pid_in_group // group_size_m + start_m = pid_m * BLOCK_SIZE_M + start_n = pid_n * BLOCK_SIZE_N + offs_am = start_m + tl.arange(0, BLOCK_SIZE_M) + offs_bn = start_n + tl.arange(0, BLOCK_SIZE_N) + offs_am = tl.where(offs_am < M, offs_am, 0) + offs_bn = tl.where(offs_bn < N, offs_bn, 0) + offs_am = tl.max_contiguous(tl.multiple_of(offs_am, BLOCK_SIZE_M), BLOCK_SIZE_M) + offs_bn = tl.max_contiguous(tl.multiple_of(offs_bn, BLOCK_SIZE_N), BLOCK_SIZE_N) + offs_k = tl.arange(0, BLOCK_SIZE_K) + a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) + b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn) + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) + for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)): + a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0) + b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0) + accumulator = tl.dot(a, b, accumulator) + a_ptrs += BLOCK_SIZE_K * stride_ak + b_ptrs += BLOCK_SIZE_K * stride_bk + c = accumulator.to(c_ptr.dtype.element_ty) + offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :] + c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N) + tl.store(c_ptrs, c, mask=c_mask) + +def matmul(a, b): + configs = {torch.float16: {'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8, 'num_stages': 1, 'num_warps': 8}, torch.float32: {'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8, 'num_stages': 1, 'num_warps': 8}} + assert a.shape[1] == b.shape[0], 'Incompatible dimensions' + assert a.dtype == b.dtype, 'Incompatible dtypes' + M, K = a.shape + K, N = b.shape + dtype = a.dtype + c = torch.empty((M, N), device=a.device, dtype=dtype) + grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),) + matmul_kernel[grid](a, b, c, M, N, K, a.stride(0), a.stride(1), b.stride(0), b.stride(1), c.stride(0), c.stride(1), BLOCK_SIZE_M=configs[dtype]['BLOCK_SIZE_M'], BLOCK_SIZE_N=configs[dtype]['BLOCK_SIZE_N'], BLOCK_SIZE_K=configs[dtype]['BLOCK_SIZE_K'], GROUP_SIZE_M=configs[dtype]['GROUP_SIZE_M'], num_stages=configs[dtype]['num_stages'], num_warps=configs[dtype]['num_warps']) + return c + +################################################################################################################################################## + + + + + +import torch + + + +# Test for matmul + +def test_matmul(): + + results = {} + + M, K, N = 256, 128, 256 + + + + # Test case 1: torch.float16 + + a = torch.randn((M, K), dtype=torch.float16, device='cuda') + + b = torch.randn((K, N), dtype=torch.float16, device='cuda') + + c = matmul(a, b) + + results['test_case_1'] = c + + + + return results + + + +# Run all tests + +result_gold = test_matmul() \ No newline at end of file diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/triton_matmul.py_gen_triton_code_949726.py.stderr b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/triton_matmul.py_gen_triton_code_949726.py.stderr new file mode 100644 index 0000000..e69de29 diff --git a/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/triton_matmul.py_gen_triton_code_949726.py.stdout b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/triton_matmul.py_gen_triton_code_949726.py.stdout new file mode 100644 index 0000000..10f2a81 --- /dev/null +++ b/reflexion_oneshot_tritonbench_iter1_4/tmp/tmp/gen/triton_matmul.py_gen_triton_code_949726.py.stdout @@ -0,0 +1 @@ +True*#*#True*#*#None*#*#None \ No newline at end of file diff --git a/src/agents/reflexion_oneshot.py b/src/agents/reflexion_oneshot.py index d10a345..c0549b9 100644 --- a/src/agents/reflexion_oneshot.py +++ b/src/agents/reflexion_oneshot.py @@ -53,17 +53,29 @@ class Memory(metaclass=MemoryClassMeta, field_names=["ps", for ps in self.dataset.problem_states: if ps.label: fs_mem = extract_function_signatures(ps.label) + elif ps.ref_code is not None: + fs_mem = extract_function_signatures(ps.ref_code) else: fs_mem = None if mem_file is None: - os_mem = self.instruction_retriever.query(ps.instruction)[0] - tmp_mem = Memory(ps=ps, - err_msg=None, - reflection=None, - function_signatures=fs_mem, - oneshot=os_mem["code"], - pass_call=False, - ) + if ps.ref_code is not None: + # read the reference code from the reference path + tmp_mem = Memory(ps=ps, + err_msg=None, + reflection=None, + function_signatures=fs_mem, + oneshot=ps.ref_code, + pass_call=False, + ) + else: + os_mem = self.instruction_retriever.query(ps.instruction)[0] + tmp_mem = Memory(ps=ps, + err_msg=None, + reflection=None, + function_signatures=fs_mem, + oneshot=os_mem["code"], + pass_call=False, + ) else: input_mem = input_mems[ps.filename] tmp_mem = Memory(ps=ps, @@ -126,9 +138,16 @@ def run(self, output_path=None, multi_thread=True, verbose=False, datalen=None, logger.info(f"\nrun scripts on gpu") for mem in tqdm(self.memories[:data_len]): if mem.pass_call: + logger.info(f"{mem.ps.filename} is_pass: {mem.pass_call}") continue is_pass, err_msg = self.dataset.run_single_call(mem.ps) - if not is_pass: + logger.info(f"{mem.ps.filename} is_pass: {is_pass}, err_msg: {err_msg}") + if is_pass: + mem.pass_call = True + mem.err_msg = None + mem.reflection = None + else: + mem.pass_call = False mem.err_msg = err_msg """ To measure kernel latency, follow these steps: @@ -176,9 +195,9 @@ def generate_solution(self, mem, temperature=0): # tab = "\n" # fss_text = "".join(f"* {sig}{tab}" for sig in mem.function_signatures) - text = prompt_for_generation.prompt.format( + text = prompt_for_generation.my_prompt.format( instruction=mem.ps.instruction, - function_signatures="" + function_signatures=mem.function_signatures if mem.function_signatures else "" ) if not mem.ps.solution: @@ -209,7 +228,7 @@ def generate_solution(self, mem, temperature=0): def generate_reflexion(self, mem, temperature): if mem.pass_call: return - reflect_txt = prompt_for_reflection.prompt.format( + reflect_txt = prompt_for_reflection.my_prompt.format( problem=mem.ps.instruction, solution=mem.ps.solution, test_result=mem.err_msg diff --git a/src/configs/tritonbench_oneshot_config.yaml b/src/configs/tritonbench_oneshot_config.yaml index b54bc4c..9be4d78 100644 --- a/src/configs/tritonbench_oneshot_config.yaml +++ b/src/configs/tritonbench_oneshot_config.yaml @@ -1,7 +1,7 @@ # LLM model api_key: "" model_id: "Kimi-K2-Instruct" -temperature: 1.0 +temperature: 0.2 # TritonBench statis_path: "/hackathon-agent/src/dataloaders/TB_eval/data/TritonBench_G_comp_alpac_v1_hackathon.json" @@ -21,9 +21,12 @@ py_interpreter: "python" # target_kernels: ["flash_decode2_phi.py", 'l2_norm_triton1.py', "int4_matmul.py", "sin_kernel.py", "triton_matmul.py", "l2_norm_bwd.py", "matrix_transpose.py", "embedding_triton_kernel.py", "rotary_transform.py", "matrix_vector_multip.py"] # you can specify which kernels you want to generate by setting target_kernels. null means all 10 kernels for hackathon. target_kernels: null +# target_kernels: ["matrix_vector_multip.py"] # the path where results will be stored -output_path: "/workspace/reflexion_oneshot_tritonbench.json" +output_path: "/workspace/reflexion_oneshot_tritonbench_iter1.json" +# output_path: "/workspace/reflexion_oneshot_tritonbench_matrix_vector_multip.json" +# output_path: "/workspace/reflexion_oneshot_tritonbench_embedding.json" max_iteration: 5 # set multi_thread to false if you want to debug the process multi_thread: true diff --git a/src/dataloaders/ProblemState.py b/src/dataloaders/ProblemState.py index 7d90df0..c5ed1e0 100644 --- a/src/dataloaders/ProblemState.py +++ b/src/dataloaders/ProblemState.py @@ -9,6 +9,7 @@ class ProblemState: test_code: Optional[str] = None instruction: Optional[str] = None solution: Optional[str] = None + ref_code: Optional[str] = None @dataclass class ProblemStateROCm: diff --git a/src/dataloaders/TB_eval/data/TritonBench_G_comp_alpac_v1_hackathon.json b/src/dataloaders/TB_eval/data/TritonBench_G_comp_alpac_v1_hackathon.json index 8171858..c9d3c1d 100644 --- a/src/dataloaders/TB_eval/data/TritonBench_G_comp_alpac_v1_hackathon.json +++ b/src/dataloaders/TB_eval/data/TritonBench_G_comp_alpac_v1_hackathon.json @@ -1 +1 @@ -[{"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The provided Triton operator code implements a matrix-vector multiplication kernel, `mv_kernel`, designed for efficient execution on NVIDIA GPUs. It leverages Triton's Just-In-Time (JIT) compilation and auto-tuning features. The kernel function multiplies a matrix `A` of size N x M with a vector `B` of size M, resulting in an output vector `C` of size N. The kernel is parameterized by constants `BLOCK_N` and `BLOCK_M` which determine the tile sizes for the computation, allowing for optimal parallel processing. Within `mv_kernel`, a loop iterates over the matrix A in blocks of `BLOCK_M` to load sub-matrices and the corresponding block from vector B, performing element-wise multiplication and accumulation in shared memory. The partial results are then reduced along the rows and stored in the output vector `C`. The `mv` function wraps this kernel execution, asserting dimension compatibility and managing CUDA resources for launching the kernel with calculated grid dimensions.\n ", "input": "", "output": "import logging\n\nimport torch\nimport triton\nimport triton.language as tl\n\n\n@triton.autotune(\n configs=[\n triton.Config({\"BLOCK_M\": m, \"BLOCK_N\": n}, num_stages=s, num_warps=w)\n for m in [32, 64, 128]\n for n in [1, 2, 4, 8]\n for s in [3, 4]\n for w in [4, 8]\n ],\n key=[\"M\", \"N\"],\n)\n@triton.jit\ndef mv_kernel(\n A,\n B,\n C,\n N,\n M,\n stride_an,\n stride_am,\n stride_bm,\n stride_cn,\n BLOCK_N: tl.constexpr,\n BLOCK_M: tl.constexpr,\n):\n pid = tl.program_id(0)\n offset_n = pid * BLOCK_N + tl.arange(0, BLOCK_N)[:, None]\n offset_m = tl.arange(0, BLOCK_M)[None, :]\n n_mask = offset_n < N\n A_ptrs = A + offset_n * stride_an + offset_m * stride_am\n B_ptrs = B + offset_m * stride_bm\n acc = tl.zeros((BLOCK_N, BLOCK_M), dtype=tl.float32)\n for m in range(0, M, BLOCK_M):\n m_mask = m + offset_m < M\n a = tl.load(A_ptrs, mask=n_mask & m_mask, other=0.0).to(tl.float32)\n b = tl.load(B_ptrs, mask=m_mask, other=0.0).to(tl.float32)\n acc += a * b\n A_ptrs += BLOCK_M * stride_am\n B_ptrs += BLOCK_M * stride_bm\n\n acc = tl.sum(acc, axis=1)\n C_ptrs = C + offset_n * stride_cn\n tl.store(C_ptrs, acc[:, None], mask=n_mask)\n\n\ndef mv(inp, vec):\n logging.debug(\"GEMS MV\")\n assert inp.shape[1] == vec.shape[0], \"incompatible dimensions\"\n N, M = inp.shape\n out = torch.empty((N,), device=inp.device, dtype=inp.dtype)\n grid = lambda META: (triton.cdiv(N, META[\"BLOCK_N\"]),)\n with torch.cuda.device(inp.device):\n mv_kernel[grid](\n inp,\n vec,\n out,\n N,\n M,\n inp.stride(0),\n inp.stride(1),\n vec.stride(0),\n out.stride(0),\n )\n return out\n\n\n\n\n", "file": "matrix_vector_multip.py", "difficulty": "4"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The provided Triton kernel, `matmul_kernel`, is a specialized GPU matrix multiplication operation. \n It employs a blocked tiling strategy for efficient computation of the result matrix `c` from input matrices `a` and `b`. \n Within this kernel, operations are parallelized across blocks defined by BLOCK_SIZE_M, BLOCK_SIZE_N, and BLOCK_SIZE_K. \n These blocks allow the kernel to load sub-matrices, perform computations, and manage memory more efficiently.\n\n The kernel begins by computing indices for thread execution, segmenting the operation across various program IDs derived from the grid dimensions. \n For each thread block, it computes offsets `offs_am`, `offs_bn`, and `offs_k` to read data from the input matrices.\n\n In a loop iterating over slices of the K dimension, sub-matrices are loaded using `tl.load` with masks to handle boundary conditions. \n These matrices are then multiplied using `tl.dot`, accumulating results in a local accumulator. \n Memory access patterns are optimized using `tl.max_contiguous` and `tl.multiple_of` to align data in cache-friendly ways.\n\n The function finally writes the accumulated results to the output matrix `c`, with care taken to respect bounds and using conditional storage via `tl.store`.\n\n The `matmul` function wraps this kernel, preparing inputs and meta-parameters based on the matrix data types and dimensions. \n It enforces input compatibility, establishes execution grid dimensions, and sets device memory for output. \n Configuration parameters such as BLOCK_SIZE_M, num_stages, and num_warps are determined per data type, \n ensuring optimal kernel execution tailored for either float16 or Triton's experimental float8 types.\n ", "input": "", "output": "\nimport torch\nimport triton\nimport triton.language as tl\n\n\ndef _matmul_launch_metadata(grid, kernel, args):\n ret = {}\n M, N, K = args[\"M\"], args[\"N\"], args[\"K\"]\n ret[\"name\"] = f\"{kernel.name} [M={M}, N={N}, K={K}]\"\n if \"c_ptr\" in args:\n bytes_per_elem = args[\"c_ptr\"].element_size()\n else:\n bytes_per_elem = 1 if args[\"FP8_OUTPUT\"] else 2\n ret[f\"flops{bytes_per_elem * 8}\"] = 2. * M * N * K\n ret[\"bytes\"] = bytes_per_elem * (M * K + N * K + M * N)\n return ret\n\n\n@triton.jit(launch_metadata=_matmul_launch_metadata)\ndef matmul_kernel(a_ptr, b_ptr, c_ptr, #\n M, N, K, #\n stride_am, stride_ak, #\n stride_bk, stride_bn, #\n stride_cm, stride_cn, #\n BLOCK_SIZE_M: tl.constexpr, #\n BLOCK_SIZE_N: tl.constexpr, #\n BLOCK_SIZE_K: tl.constexpr, #\n GROUP_SIZE_M: tl.constexpr, #\n ):\n pid = tl.program_id(axis=0)\n num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)\n num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)\n num_pid_in_group = GROUP_SIZE_M * num_pid_n\n group_id = pid // num_pid_in_group\n first_pid_m = group_id * GROUP_SIZE_M\n group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)\n pid_m = first_pid_m + (pid % group_size_m)\n pid_n = (pid % num_pid_in_group) // group_size_m\n\n start_m = pid_m * BLOCK_SIZE_M\n start_n = pid_n * BLOCK_SIZE_N\n\n offs_am = start_m + tl.arange(0, BLOCK_SIZE_M)\n offs_bn = start_n + tl.arange(0, BLOCK_SIZE_N)\n offs_am = tl.where(offs_am < M, offs_am, 0)\n offs_bn = tl.where(offs_bn < N, offs_bn, 0)\n\n offs_am = tl.max_contiguous(tl.multiple_of(offs_am, BLOCK_SIZE_M), BLOCK_SIZE_M)\n offs_bn = tl.max_contiguous(tl.multiple_of(offs_bn, BLOCK_SIZE_N), BLOCK_SIZE_N)\n offs_k = tl.arange(0, BLOCK_SIZE_K)\n a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)\n b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)\n\n accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)\n\n for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):\n a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)\n b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)\n accumulator = tl.dot(a, b, accumulator)\n a_ptrs += BLOCK_SIZE_K * stride_ak\n b_ptrs += BLOCK_SIZE_K * stride_bk\n\n if (c_ptr.dtype.element_ty == tl.float8e4nv):\n c = accumulator.to(tl.float8e4nv)\n else:\n c = accumulator.to(tl.float16)\n\n offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)\n offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]\n c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)\n tl.store(c_ptrs, c, mask=c_mask)\n\n\ndef matmul(a, b):\n configs = {\n torch.float8_e4m3fn: {\n \"BLOCK_SIZE_M\": 128, \"BLOCK_SIZE_N\": 256, \"BLOCK_SIZE_K\": 128, \"GROUP_SIZE_M\": 8, \"num_stages\": 4,\n \"num_warps\": 8\n }, torch.float16: {\n \"BLOCK_SIZE_M\": 128, \"BLOCK_SIZE_N\": 256, \"BLOCK_SIZE_K\": 64, \"GROUP_SIZE_M\": 8, \"num_stages\": 3,\n \"num_warps\": 8\n }\n }\n # Check constraints.\n assert a.shape[1] == b.shape[0], \"Incompatible dimensions\"\n assert a.dtype == b.dtype, \"Incompatible dtypes\"\n M, K = a.shape\n K, N = b.shape\n dtype = a.dtype\n\n c = torch.empty((M, N), device=a.device, dtype=dtype)\n # 1D launch kernel where each block gets its own program.\n grid = lambda META: (triton.cdiv(M, META[\"BLOCK_SIZE_M\"]) * triton.cdiv(N, META[\"BLOCK_SIZE_N\"]), )\n matmul_kernel[grid](\n a, b, c, #\n M, N, K, #\n a.stride(0), a.stride(1), #\n b.stride(0), b.stride(1), #\n c.stride(0), c.stride(1), #\n BLOCK_SIZE_M=configs[dtype][\"BLOCK_SIZE_M\"], #\n BLOCK_SIZE_N=configs[dtype][\"BLOCK_SIZE_N\"], #\n BLOCK_SIZE_K=configs[dtype][\"BLOCK_SIZE_K\"], #\n GROUP_SIZE_M=configs[dtype][\"GROUP_SIZE_M\"], #\n num_stages=configs[dtype][\"num_stages\"], #\n num_warps=configs[dtype][\"num_warps\"], #\n )\n return c\n\n\n\n\n", "file": "triton_matmul.py", "difficulty": "3"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The Triton-accelerated function embedding_kernel is specialized for extracting and storing embedding vectors from a weight matrix for a sequence of token IDs. It uses program IDs to determine processing offsets and handles iteration over sequences with BLOCK_N and BLOCK_NN stride sizes. For each sequence, it computes token IDs and uses masks to ensure only valid data is loaded and processed. The weight matrix is addressed using a combination of token IDs and dimension offsets, facilitated by the stride of the weight tensor. The processed vectors are then stored into the 'out' tensor using calculated strides and masks, ensuring each output sequence position receives the correct embedding vector. The wrapping function, embedding, configures and invokes the kernel with appropriate grid settings, aligning BLOCK_DMODEL to the next power of two based on weight dimensions and leveraging constant memory settings to optimize the embedding extraction process.\n ", "input": "", "output": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef embedding_kernel(\n weight,\n input_ids,\n out,\n vob_start_id,\n vob_end_id,\n stride_weight_seq,\n stride_out_seq,\n n_ctx,\n hiden_size: tl.constexpr,\n BLOCK_DMODEL: tl.constexpr,\n BLOCK_N: tl.constexpr,\n BLOCK_NN: tl.constexpr,\n):\n start_n = tl.program_id(0) * BLOCK_N\n\n offs_nn = start_n + tl.arange(0, BLOCK_NN)\n offs_d = tl.arange(0, BLOCK_DMODEL)\n\n for start_nn in range(0, BLOCK_N, BLOCK_NN):\n start_nn = tl.multiple_of(start_nn, BLOCK_NN)\n offs_seq = start_nn + offs_nn\n n_ctx_mask = offs_seq < n_ctx\n token_ids = tl.load(input_ids + offs_seq, mask=n_ctx_mask, other=vob_end_id)\n id_mask = (token_ids >= vob_start_id) & (token_ids < vob_end_id)\n token_ids = token_ids - vob_start_id\n dim_mask = offs_d < hiden_size\n load_mask = id_mask[:, None] & dim_mask[None, :]\n store_mask = n_ctx_mask[:, None] & dim_mask[None, :]\n vecs = tl.load(weight + token_ids[:, None] * stride_weight_seq + offs_d[None, :], mask=load_mask, other=0.0)\n tl.store(out + offs_seq[:, None] * stride_out_seq + offs_d[None, :], vecs, mask=store_mask)\n\n@torch.no_grad()\ndef embedding(input_ids, weight: torch.Tensor, vob_start_id, vob_end_id, out: torch.Tensor):\n BLOCK_N = 64\n BLOCK_NN = 1\n BLOCK_DMODEL = triton.next_power_of_2(weight.shape[1])\n n_ctx = input_ids.shape[0]\n\n grid = (triton.cdiv(n_ctx, BLOCK_N), 1, 1)\n\n embedding_kernel[grid](\n weight,\n input_ids,\n out,\n vob_start_id,\n vob_end_id,\n weight.stride(0),\n out.stride(0),\n n_ctx=n_ctx,\n hiden_size=weight.shape[1],\n BLOCK_DMODEL=BLOCK_DMODEL,\n BLOCK_N=BLOCK_N,\n BLOCK_NN=BLOCK_NN,\n num_warps=1,\n num_stages=1,\n )\n\n\n\n\n", "file": "embedding_triton_kernel.py", "difficulty": "3"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The code defines a Triton-based kernel for matrix multiplication of INT4 quantized weights and provides Python functions to handle the quantization and dequantization processes. \n The 'matmul_kernel' function is a Triton kernel using @triton.jit which performs matrix multiplication. It processes the input matrices in tiles defined by BLOCK_SIZE_M, BLOCK_SIZE_N, and BLOCK_SIZE_K, and uses a loop to iterate over the K dimension to accumulate results in fp32 precision, subsequently stored in the output buffer, potentially employing atomic add for SPLIT_K > 1.\n The kernel is set up with a wide variety of configurations, allowing Triton's autotuning capabilities to select optimal parameters based on matrix dimensions M, N, and K. It utilizes quantized INT4 weights, reconstructing them using scales and zero points to compute matrix products accurately. \n The Python function 'matmul_dequantize_int4_s2' serves as an interface to this kernel, preparing data and launching the computation on specified grid dimensions.\n The function 'quantize_int4' converts a floating-point weight matrix into INT4 format, organizing the data by packing 8 INT4 values into one INT32 and calculating scaling factors and zero points for each group of elements. \n The helper function 'unpack_int4' is intended for testing; it unpacks the INT4 matrix back into a floating-point format to verify the quantization process.\n ", "input": "", "output": "import time\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.autotune(\n\tconfigs=[\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n\t \n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n\t\t\n ],\n\tkey=['M', 'N', 'K'],\n reset_to_zero=['c_ptr']\n)\n@triton.jit\ndef matmul_kernel(\n a_ptr, b_ptr, c_ptr,\n bs_ptr, bzp_ptr,\n M, N, K,\n stride_am, stride_ak,\n stride_bk, stride_bn,\n stride_cm, stride_cn,\n stride_bsk, stride_bsn,\n stride_bzpk, stride_bzpn,\n group_size,\n BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,\n GROUP_SIZE_M: tl.constexpr, SPLIT_K: tl.constexpr\n ):\n \"\"\"\n assert K % (BLOCK_SIZE_K * SPLIT_K) == 0\n \"\"\"\n pid = tl.program_id(axis=0)\n pid_sp_k = tl.program_id(axis=1)\n num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)\n num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)\n num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)\n num_pid_in_group = GROUP_SIZE_M * num_pid_n\n group_id = pid // num_pid_in_group\n first_pid_m = group_id * GROUP_SIZE_M\n group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)\n pid_m = first_pid_m + (pid % group_size_m)\n pid_n = (pid % num_pid_in_group) // group_size_m \n offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M\n offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N\n offs_k = pid_sp_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)\n\n # [BLOCK_M, BLOCK_K]\n a_ptrs = a_ptr + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak\n # [BLOCK_K, BLOCK_N] but repeated 8 times in N\n b_ptrs = b_ptr + (offs_k[:, None] // 8) * stride_bk + offs_bn[None, :] * stride_bn\n # tl.static_print(\"shape\", a_ptrs, b_ptrs, bs_ptrs, bzp_ptrs)\n # -----------------------------------------------------------\n # Iterate to compute a block of the C matrix.\n # We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block\n # of fp32 values for higher accuracy.\n # `accumulator` will be converted back to fp16 after the loop.\n accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)\n for k in range(0, tl.cdiv(K, BLOCK_SIZE_K * SPLIT_K)):\n # Load the next block of A and B.\n # [BLOCK_K, BLOCK_N] but repeated group_size times in K \n bs_ptrs = bs_ptr + ((offs_k[:, None] + k * BLOCK_SIZE_K * SPLIT_K) // group_size) * stride_bsk \\\n + offs_bn[None, :] * stride_bsn\n # [BLOCK_K, BLOCK_N] but repeated in K and N\n bzp_ptrs = bzp_ptr + ((offs_k[:, None] + k * BLOCK_SIZE_K * SPLIT_K) // group_size) * stride_bzpk \\\n + (offs_bn[None, :] // 8) * stride_bzpn\n b_shift_bits = (offs_k[:, None] % 8) * 4 # assert BLOCK_SIZE_K % 8 == 0\n bzp_shift_bits = (offs_bn[None, :] % 8) * 4\n a = tl.load(a_ptrs)\n b = tl.load(b_ptrs)\n bs = tl.load(bs_ptrs)\n bzp = tl.load(bzp_ptrs)\n # We accumulate along the K dimension.\n int_b = (b >> b_shift_bits) & 0xF\n int_bzp = (bzp >> bzp_shift_bits) & 0xF\n b = ((int_b - int_bzp) * bs).to(a.dtype)\n accumulator += tl.dot(a, b.to(a.dtype))\n # Advance the ptrs to the next K block.\n a_ptrs += BLOCK_SIZE_K * SPLIT_K * stride_ak\n b_ptrs += (BLOCK_SIZE_K * SPLIT_K * stride_bk // 8) # assert BLOCK_SIZE_K % 8 == 0\n # You can fuse arbitrary activation functions here\n # while the accumulator is still in FP32!\n c = accumulator.to(c_ptr.dtype.element_ty)\n # -----------------------------------------------------------\n # Write back the block of the output matrix C with masks.\n offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)\n offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]\n c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)\n if SPLIT_K == 1:\n tl.store(c_ptrs, c, mask=c_mask)\n else:\n tl.atomic_add(c_ptrs, c, mask=c_mask)\n\n\ndef matmul_dequantize_int4_s2(x: torch.FloatTensor, qweight: torch.IntTensor, scales: torch.FloatTensor, qzeros: torch.IntTensor, group_size: int = 128, output=None) -> torch.FloatTensor:\n \"\"\"\n \"\"\"\n assert x.is_contiguous(), \"A must be contiguous\"\n assert qweight.is_contiguous(), \"B must be contiguous\" \n M, K = x.shape\n N = scales.shape[1]\n if output is None:\n output = torch.zeros((M, N), device=x.device, dtype=x.dtype) \n grid = lambda META: (\n triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),\n META['SPLIT_K'],\n )\n matmul_kernel[grid](\n x, qweight, output,\n scales, qzeros,\n M, N, K,\n x.stride(0), x.stride(1),\n qweight.stride(0), qweight.stride(1),\n output.stride(0), output.stride(1),\n scales.stride(0), scales.stride(1),\n qzeros.stride(0), qzeros.stride(1),\n group_size,\n )\n return output\n\ndef quantize_int4(weight, group_size=128, tp_rank=0):\n # Weight shape: [H1 // 8, H2]\n # Scale shape: [H1 // group_size, H2]\n # zero_pint shape: [H1 // group_size, H2 // 8]\n\n weight = weight.transpose(1, 0)\n h1, h2 = weight.shape\n assert h1 % 8 == 0 and h2 % 8 == 0, \"H1 {} H2 {}\".format(h1, h2)\n assert h2 % group_size == 0, \"H1 {} H2 {}\".format(h1, h2)\n weight = weight.contiguous().view(-1, group_size).cuda(tp_rank)\n weight_max = weight.amax(-1, keepdim=True)\n weight_max = torch.where(weight_max < 0, 0, weight_max)\n weight_min = weight.amin(-1, keepdim=True)\n weight_min = torch.where(weight_min > 0, 0, weight_min)\n weight_range = weight_max - weight_min \n scale = weight_range / (2 ** 4 - 1)\n zero_point = (-weight_min / scale).round().clamp(0, 15).to(torch.int32)\n weight = (weight / scale + zero_point).round().clamp(0, 15).to(torch.int32).view(h1, h2)\n int_weight = torch.empty(h1, h2 // 8).to(torch.int32).to(weight.device)\n int_zero_point = torch.zeros(h1 // 8, h2 // group_size).to(torch.int32).to(weight.device)\n zero_point = zero_point.view(h1, -1)\n scale = scale.view(h1, -1)\n # pack 8 int4 in an int32 number.\n # Weight pack in row.\n for pack in range(0, h2, 8):\n for i in range(8):\n int_weight[:, pack // 8] += weight[:, pack + i] << (i * 4)\n # zero point pack in col.\n for pack in range(0, h1, 8):\n for i in range(8):\n int_zero_point[pack // 8, :] += zero_point[pack + i, :] << (i * 4)\n '''\n fp_weight = torch.zeros(h1, h2).half().to(weight.device)\n for pack in range(0, h1 // 8):\n for i in range(8):\n fp_weight[pack * 8 + i, :] = \\\n ((int_weight[pack, :] << (28 - i * 4) >> 28) + 16) % 16\n print((fp_weight - weight).abs().sum())\n\n fp_zp = torch.zeros(zero_point.shape).half().to(zero_point.device)\n for pack in range(0, h1 // 8):\n for i in range(8):\n fp_zp[pack * 8 + i, :] = \\\n (int_zero_point[pack, :] >> (i * 4)) & 15\n\n print((fp_zp - zero_point).abs().sum())\n '''\n weight = None\n return int_weight.transpose(1, 0).contiguous(), scale.transpose(1, 0).contiguous(), int_zero_point.transpose(1, 0).contiguous(), group_size\n\n\ndef unpack_int4(weight, scale, zp):\n \"\"\"\n Test function to verify quantize int4 is correct.\n Will not be used in model inference.\n \"\"\"\n weight = weight.transpose(1, 0)\n scale = scale.transpose(1, 0)\n zp = zp.transpose(1, 0)\n h1, h2 = weight.shape\n group_size = h2 * 8 // scale.shape[1]\n group_num = scale.shape[1]\n fp_weight = torch.zeros(h1, h2 * 8).half().to(weight.device)\n fp_zero_point = torch.zeros(h1, group_num).to(weight.device)\n for pack in range(0, h2):\n for i in range(8):\n fp_weight[:, pack * 8 + i] = (weight[:, pack] >> (i * 4)) & 0xF\n for pack in range(0, h1 // 8):\n for i in range(8):\n fp_zero_point[pack * 8 + i, :] = (zp[pack, :] >> (i * 4)) & 0xF\n for g in range(group_num):\n fp_weight[:, g * group_size:(g + 1) * group_size] = (fp_weight[:, g * group_size:(g + 1) * group_size] - \\\n fp_zero_point[:, g].unsqueeze(1)) * scale[:, g].unsqueeze(1)\n return fp_weight.transpose(1, 0)\n\n\n\n", "file": "int4_matmul.py", "difficulty": "5"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The `_fwd_kernel_flash_decode_stage2` Triton kernel is a parallel computation designed for processing sequences in a neural network context, specifically dealing with batches, heads, and sequence blocks. This kernel receives several inputs: `B_Seqlen`, `Mid_O`, `Mid_O_LogExpSum`, and `Out`, along with strides for indexing. `B_Seqlen` contains sequence lengths per batch, `Mid_O` contains intermediate outputs, `Mid_O_LogExpSum` holds log-exp sum values, and `Out` will store the final output. The kernel operates over a 2D grid defined by batch size and head count (`grid = (batch, head_num)`), with constants `BLOCK_SEQ` and `BLOCK_DMODEL` indicating sequence block size and dimension alignment respectively.\n\n The kernel function operates as follows:\n - Identifies the current batch and head using `tl.program_id`.\n - Initializes accumulators: `sum_exp`, `max_logic`, and `acc` to accumulate exponential logic and values.\n - Loads the current sequence length and calculates the number of sequence blocks (`block_n_size`).\n - Iterates over each block, where:\n - It loads values (`tv`) from `Mid_O` and logic sums (`tlogic`) from `Mid_O_LogExpSum`.\n - Computes the maximum logic value across blocks and scales previous accumulations.\n - Updates the accumulators by computing the exponential of adjusted logic values and scaling/accumulating.\n - Stores the final normalized result into `Out`, scaling accumulated values by the sum of exponentials.\n\n The `flash_decode_stage2` function sets up and invokes this kernel, determining dimensions and grid setup based on input tensor shapes. It ensures efficient computation by using Triton's parallel execution framework, specifying warp and stage numbers.\n ", "input": "", "output": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef _fwd_kernel_flash_decode_stage2(\n B_Seqlen,\n Mid_O, # [batch, head, seq_block_num, head_dim]\n Mid_O_LogExpSum, # [batch, head, seq_block_num]\n Out, # [batch, head, head_dim]\n stride_mid_ob,\n stride_mid_oh,\n stride_mid_os,\n stride_mid_od,\n stride_mid_o_eb,\n stride_mid_o_eh,\n stride_mid_o_es,\n stride_obs,\n stride_oh,\n stride_od,\n head_dim,\n BLOCK_SEQ: tl.constexpr,\n BLOCK_DMODEL: tl.constexpr,\n):\n cur_batch = tl.program_id(0)\n cur_head = tl.program_id(1)\n\n offs_d = tl.arange(0, BLOCK_DMODEL)\n cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)\n\n block_n_size = tl.where(cur_batch_seq_len <= 0, 0, cur_batch_seq_len + BLOCK_SEQ - 1) // BLOCK_SEQ\n\n sum_exp = 0.0\n max_logic = -float(\"inf\")\n acc = tl.zeros([BLOCK_DMODEL], dtype=tl.float32)\n\n offs_v = cur_batch * stride_mid_ob + cur_head * stride_mid_oh + offs_d\n offs_logic = cur_batch * stride_mid_o_eb + cur_head * stride_mid_o_eh\n for block_seq_n in range(0, block_n_size, 1):\n tv = tl.load(Mid_O + offs_v + block_seq_n * stride_mid_os, mask=offs_d < head_dim, other=0.0)\n tlogic = tl.load(Mid_O_LogExpSum + offs_logic + block_seq_n)\n new_max_logic = tl.maximum(tlogic, max_logic)\n\n old_scale = tl.exp(max_logic - new_max_logic)\n acc *= old_scale\n exp_logic = tl.exp(tlogic - new_max_logic)\n acc += exp_logic * tv\n sum_exp = sum_exp * old_scale + exp_logic\n max_logic = new_max_logic\n\n tl.store(Out + cur_batch * stride_obs + cur_head * stride_oh + offs_d, acc / sum_exp, mask=offs_d < head_dim)\n return\n\n@torch.no_grad()\ndef flash_decode_stage2(mid_out, mid_out_logexpsum, B_Seqlen, Out, block_seq):\n Lk = mid_out.shape[-1]\n head_dim = Lk\n batch, head_num = mid_out.shape[0], mid_out.shape[1]\n BLOCK_DMODEL = triton.next_power_of_2(head_dim)\n grid = (batch, head_num)\n\n _fwd_kernel_flash_decode_stage2[grid](\n B_Seqlen,\n mid_out,\n mid_out_logexpsum,\n Out,\n mid_out.stride(0),\n mid_out.stride(1),\n mid_out.stride(2),\n mid_out.stride(3),\n mid_out_logexpsum.stride(0),\n mid_out_logexpsum.stride(1),\n mid_out_logexpsum.stride(2),\n Out.stride(0),\n Out.stride(1),\n Out.stride(2),\n head_dim,\n BLOCK_SEQ=block_seq,\n BLOCK_DMODEL=BLOCK_DMODEL,\n num_warps=4,\n num_stages=2,\n )\n return\n\n\n\n\n", "file": "flash_decode2_phi.py", "difficulty": "2"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\nThe Triton operator is defined to transpose a matrix using a kernel function and a wrapper function. The kernel function named 'kernel' is decorated with '@triton.jit' for just-in-time compilation and performs matrix transposition by directly manipulating pointers based on the given strides and dimensions. It accepts input parameters such as a matrix 'M', an output buffer 'Out', the strides of 'M' and 'Out', and the dimensions 'SIZE_M' and 'D_HEAD'. The kernel computes the pointers for elements of 'M' using 'matrix_stridex' and 'matrix_stridey', and for 'Out' using 'out_stridex' and 'out_stridey'. The transposition is achieved by loading elements from 'M' and storing them into 'Out' in a transposed layout. The wrapper function named 'wrapper' initializes 'matrix' with random float16 values and 'out' with zeros, both on CUDA. It defines the grid configuration as a tuple with a single element, then calls the kernel with these matrices and their properties. Finally, it returns the transposed matrix 'out'.\n ", "input": "", "output": "import torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef kernel(\n M,\n Out,\n matrix_stridex,\n matrix_stridey,\n out_stridex,\n out_stridey,\n SIZE_M: tl.constexpr,\n D_HEAD: tl.constexpr,\n):\n size_m_arange = tl.arange(0, SIZE_M)\n d_head_arange = tl.arange(0, D_HEAD)\n # transpose\n matrix_ptr = M + d_head_arange[None, :] * matrix_stridey + size_m_arange[:, None] * matrix_stridex\n out_ptr = Out + d_head_arange[None, :] * out_stridex + size_m_arange[:, None] * out_stridey\n matrix = tl.load(matrix_ptr)\n tl.store(out_ptr, matrix)\n\ndef wrapper(size_m, d_head):\n matrix = torch.randn((size_m, d_head), dtype=torch.float16, device=\"cuda\")\n out = torch.zeros((d_head, size_m), dtype=torch.float16, device=\"cuda\")\n\n grid = (1,)\n kernel[grid](\n matrix,\n out,\n *matrix.stride(),\n *out.stride(),\n size_m,\n d_head,\n )\n return out\n\n\n\n", "file": "matrix_transpose.py", "difficulty": "2"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The `rotary_kernel` function is a Triton kernel that performs rotary position encoding on a tensor `X` using precomputed cosine (`COS`) and sine (`SIN`) matrices. It modifies or populates the output tensor `OUT` with the transformed data. The kernel accommodates both fixed and variable sequence lengths, controlled by the presence of `CU_SEQLENS`. The kernel handles interleaved and non-interleaved formats and allows for in-place transformations and conjugate computations if specified.\n\n The kernel operates in a three-dimensional grid, processing batches (`pid_batch`), heads (`pid_head`), and sequences (`pid_m`). It calculates transformations by loading blocks of data and applying rotary transformations based on cosine and sine values. The key operations are tailored based on whether the data is interleaved or not, with conditional handling for conjugation using `CONJUGATE`.\n\n The `apply_rotary` function acts as a high-level interface to the Triton kernel. It accepts the input tensor `x`, cosine and sine matrices, sequence length offsets, and optional cumulative sequence lengths (`cu_seqlens`). The function determines the execution grid and block sizes, aligning them with the input data shape and configuration. It initializes an output tensor, copying non-rotary parts of `x` if required. The function ensures that the kernel is called with appropriate arguments, matching the shape and type expectations set within the kernel logic. This design allows for efficient rotary transformations in transformer architectures.\n ", "input": "", "output": "from typing import Optional, Union\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef rotary_kernel(\n OUT, X, COS, SIN, CU_SEQLENS, SEQLEN_OFFSETS, seqlen, nheads, rotary_dim, seqlen_ro,\n CACHE_KEY_SEQLEN, stride_out_batch, stride_out_seqlen, stride_out_nheads,\n stride_out_headdim, stride_x_batch, stride_x_seqlen, stride_x_nheads,\n stride_x_headdim, BLOCK_K: tl.constexpr, IS_SEQLEN_OFFSETS_TENSOR: tl.constexpr,\n IS_VARLEN: tl.constexpr, INTERLEAVED: tl.constexpr, CONJUGATE: tl.constexpr, BLOCK_M: tl.constexpr,\n):\n pid_m = tl.program_id(axis=0)\n pid_batch = tl.program_id(axis=1)\n pid_head = tl.program_id(axis=2)\n rotary_dim_half = rotary_dim // 2\n\n if not IS_VARLEN:\n X = X + pid_batch * stride_x_batch + pid_head * stride_x_nheads\n OUT = OUT + pid_batch * stride_out_batch + pid_head * stride_out_nheads\n else:\n start_idx = tl.load(CU_SEQLENS + pid_batch)\n seqlen = tl.load(CU_SEQLENS + pid_batch + 1) - start_idx\n X = X + start_idx * stride_x_seqlen + pid_head * stride_x_nheads\n OUT = OUT + start_idx * stride_out_seqlen + pid_head * stride_out_nheads\n\n if pid_m * BLOCK_M >= seqlen:\n return\n rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)\n if not IS_SEQLEN_OFFSETS_TENSOR:\n rm_cs = rm + SEQLEN_OFFSETS\n else:\n rm_cs = rm + tl.load(SEQLEN_OFFSETS + pid_batch)\n rk = tl.arange(0, BLOCK_K)\n rk_half = tl.arange(0, BLOCK_K // 2)\n\n if not INTERLEAVED:\n X = X + (rm[:, None] * stride_x_seqlen + rk_half[None, :] * stride_x_headdim)\n COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :])\n SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :])\n cos = tl.load(COS, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=1.0).to(tl.float32)\n sin = tl.load(SIN, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=0.0).to(tl.float32)\n x0 = tl.load(X, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), other=0.0).to(tl.float32)\n x1 = tl.load(X + rotary_dim_half * stride_x_headdim, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), other=0.0).to(tl.float32)\n if CONJUGATE:\n sin = -sin\n o0 = x0 * cos - x1 * sin\n o1 = x0 * sin + x1 * cos\n OUT = OUT + (rm[:, None] * stride_out_seqlen + rk_half[None, :] * stride_out_headdim)\n tl.store(OUT, o0, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half))\n tl.store(OUT + rotary_dim_half * stride_out_headdim, o1, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half))\n else:\n rk_swap = rk + ((rk + 1) % 2) * 2 - 1\n rk_repeat = tl.arange(0, BLOCK_K) // 2\n X0 = X + (rm[:, None] * stride_x_seqlen + rk[None, :] * stride_x_headdim)\n X1 = X + (rm[:, None] * stride_x_seqlen + rk_swap[None, :] * stride_x_headdim)\n COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :])\n SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :])\n cos = tl.load(COS, mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half), other=1.0).to(tl.float32)\n sin = tl.load(SIN, mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half), other=0.0).to(tl.float32)\n x0 = tl.load(X0, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim), other=0.0).to(tl.float32)\n x1 = tl.load(X1, mask=(rm[:, None] < seqlen) & (rk_swap[None, :] < rotary_dim), other=0.0).to(tl.float32)\n if CONJUGATE:\n sin = -sin\n x0_cos = x0 * cos\n x1_sin = x1 * sin\n out = tl.where(rk[None, :] % 2 == 0, x0_cos - x1_sin, x0_cos + x1_sin)\n OUT = OUT + (rm[:, None] * stride_out_seqlen + rk[None, :] * stride_out_headdim)\n tl.store(OUT, out, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim))\n\ndef apply_rotary(\n x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, seqlen_offsets: Union[int, torch.Tensor] = 0,\n cu_seqlens: Optional[torch.Tensor] = None, max_seqlen: Optional[int] = None,\n interleaved=False, inplace=False, conjugate=False\n) -> torch.Tensor:\n is_varlen = cu_seqlens is not None\n if not is_varlen:\n batch, seqlen, nheads, headdim = x.shape\n else:\n total_seqlen, nheads, headdim = x.shape\n batch = cu_seqlens.shape[0] - 1\n seqlen = max_seqlen\n seqlen_ro, rotary_dim = cos.shape\n rotary_dim *= 2\n\n cos, sin = cos.contiguous(), sin.contiguous()\n if isinstance(seqlen_offsets, torch.Tensor):\n seqlen_offsets = seqlen_offsets.contiguous()\n else:\n seqlen_offsets += seqlen\n\n output = torch.empty_like(x) if not inplace else x\n if rotary_dim < headdim and not inplace:\n output[..., rotary_dim:].copy_(x[..., rotary_dim:])\n\n BLOCK_K = 32 if rotary_dim <= 32 else (64 if rotary_dim <= 64 else (128 if rotary_dim <= 128 else 256))\n grid = lambda META: (triton.cdiv(seqlen, META[\"BLOCK_M\"]), batch, nheads)\n BLOCK_M = 4 if interleaved else (8 if rotary_dim <= 64 else 4)\n\n with torch.cuda.device(x.device.index):\n rotary_kernel[grid](\n output, x, cos, sin, cu_seqlens, seqlen_offsets, seqlen, nheads, rotary_dim,\n seqlen_ro, seqlen // 128, output.stride(0) if not is_varlen else 0, output.stride(-3),\n output.stride(-2), output.stride(-1), x.stride(0) if not is_varlen else 0,\n x.stride(-3), x.stride(-2), x.stride(-1), BLOCK_K,\n isinstance(seqlen_offsets, torch.Tensor), is_varlen, interleaved, conjugate, BLOCK_M\n )\n return output\n\n\n\n\n", "file": "rotary_transform.py", "difficulty": "4"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The code implements a Triton kernel named `kernel_function`, which processes input data using block-wise operations. \n The kernel takes pointers to input and output data (`x_ptr` and `output_ptr`), the total number of elements to process (`n_elements`), and a constant block size (`BLOCK_SIZE`). \n Inside the kernel, each program instance calculates its starting point (`block_start`) and creates an `offsets` tensor for element indexing. \n A mask ensures operations only occur on valid indices within the input bounds. The kernel loads data from `x_ptr`, computes the sine using `tl.math.sin`, and stores the result in `output_ptr`. \n The `call_kernel` function prepares to execute the kernel by calculating the total number of elements (`n_elements`) and creates an output tensor. \n It defines a grid configuration function using lambda to handle thread block calculations based on `BLOCK_SIZE`, ensuring the entire input is processed. \n The kernel is then launched with the grid configuration, input, output, and element count.\n ", "input": "", "output": "\nimport torch\nimport triton\nimport triton.language as tl\n\n# Kernel function using Triton\n@triton.jit\ndef kernel_function(x_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):\n # x_ptr: pointer to input data\n # output_ptr: pointer to output data\n # n_elements: number of elements to process\n # BLOCK_SIZE: block size for Triton kernel\n pid = tl.program_id(0)\n block_start = pid * BLOCK_SIZE\n offsets = block_start + tl.arange(0, BLOCK_SIZE)\n mask = offsets < n_elements\n x = tl.load(x_ptr + offsets, mask=mask)\n output = tl.math.sin(x)\n tl.store(output_ptr + offsets, output, mask=mask)\n\n# Function to call the Triton kernel\ndef call_kernel(x):\n # x: input tensor\n n_elements = x.numel()\n output = torch.empty_like(x)\n grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n kernel_function[grid](x, output, n_elements, BLOCK_SIZE=1024)\n return output\n\n\n\n\n", "file": "sin_kernel.py", "difficulty": "1"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The Triton kernel `_l2_norm_bwd_kernel` performs a backward pass operation for L2 normalization on a per-row basis. It receives pointers to input `X`, output gradient `DY`, and calculates the input gradient `DX`. Each row of the input is accessed using the `stride_x_row`. `BLOCK_N` determines the number of elements processed per block, set based on maximum allowable fused size and next power of 2 of `N`. Within the kernel, it computes the variance of the input slice, uses it to compute the reciprocal of the standard deviation (`rstd`), and then calculates `dx` using the formula `dy * rstd - tl.sum(dy * x) * (1 / (var+eps)) * rstd * x`. The result is conditionally stored in `DX` using masks. The `_l2_norm_bwd` function orchestrates this process, ensuring input tensors `x` and `dy` are properly reshaped and their strides configured for contiguity if necessary. If `N` exceeds `BLOCK_N`, an error is raised to prevent excessive feature dimensions. Finally, the kernel is launched over `M` rows of the reshaped tensors, and the output `dx` is reshaped back to the original input shape.\n ", "input": "", "output": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef _l2_norm_bwd_kernel(\n X, # pointer to the input\n DY, # pointer to the output gradient\n DX, # pointer to the input gradient\n stride_x_row, # how much to increase the pointer when moving by 1 row\n N, # number of columns in X\n eps, # epsilon to avoid division by zero\n BLOCK_N: tl.constexpr,\n):\n row = tl.program_id(0)\n X += row * stride_x_row\n DX += row * stride_x_row\n DY += row * stride_x_row\n cols = tl.arange(0, BLOCK_N)\n x = tl.load(X + cols, mask=cols < N, other=0.0).to(tl.float32)\n x = tl.where(cols < N, x, 0.0)\n var = tl.sum(x * x) \n rstd = 1 / tl.sqrt(var + eps)\n mask = cols < N\n dy = tl.load(DY + cols, mask=cols < N, other=0.0).to(tl.float32)\n dy = tl.where(cols < N, dy, 0.0)\n dx = dy * rstd - tl.sum(dy * x) * (1 / (var+eps)) * rstd * x\n tl.store(DX + cols, dx, mask=mask)\n\ndef _l2_norm_bwd(\n x, dy, eps=1e-5,\n):\n x_shape_og = x.shape\n x = x.reshape(-1, dy.shape[-1])\n dy = dy.reshape(-1, dy.shape[-1])\n if dy.stride(-1) != 1:\n dy = dy.contiguous()\n dx = torch.empty_like(x)\n N = x.shape[-1]\n M = x.shape[0]\n MAX_FUSED_SIZE = 65536 // x.element_size()\n BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))\n if N > BLOCK_N:\n raise RuntimeError(\n \"This layer norm doesn't support feature dim >= 64KB.\")\n with torch.cuda.device(x.device.index):\n _l2_norm_bwd_kernel[(M,)](\n x,\n dy,\n dx,\n x.stride(0),\n N,\n eps,\n BLOCK_N,\n )\n return dx.reshape(x_shape_og)\n\n\n\n\n", "file": "l2_norm_bwd.py", "difficulty": "3"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The Triton kernel `_l2_norm_fwd_1pass_kernel` aims to perform L2 normalization on a 2D input tensor `X`. It processes each row separately using Triton's parallel execution model. The kernel expects pointers to `X` and `Y` along with the stride for rows (`stride_x_row`), number of columns in `X` (`N`), a small constant `eps` to prevent division by zero, and a compile-time constant `BLOCK_N`. The kernel computes L2 normalization by first loading a block of data from `X`, calculating the sum of squares for variance, and computing the reciprocal of the square root of the variance plus `eps` to get `rstd`. It then multiplies the input block by `rstd` to produce the normalized values, which are stored in `Y`.\n\n The Python function `_l2_norm_fwd` handles the setup and execution of the kernel. It first reshapes and possibly makes the input tensor `x` contiguous. It initializes an empty tensor `y` to store the output. The function calculates `BLOCK_N` based on `x`'s element size and ensures it doesn't exceed 64KB. If the feature dimension `N` is larger than `BLOCK_N`, it raises an error. The kernel is then launched with the total number of rows `M`, pointers to `x` and `y`, stride, number of columns, `eps`, and `BLOCK_N`. Finally, the function returns the normalized tensor reshaped to its original dimensions.\n ", "input": "", "output": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef _l2_norm_fwd_1pass_kernel(\n X, # pointer to the input\n Y, # pointer to the output\n stride_x_row, # how much to increase the pointer when moving by 1 row\n N, # number of columns in X\n eps, # epsilon to avoid division by zero\n BLOCK_N: tl.constexpr,\n):\n row = tl.program_id(0)\n X += row * stride_x_row\n Y += row * stride_x_row\n cols = tl.arange(0, BLOCK_N)\n x = tl.load(X + cols, mask=cols < N, other=0.0).to(tl.float32)\n xbar = tl.where(cols < N, x, 0.0)\n var = tl.sum(xbar * xbar, axis=0) \n rstd = 1 / tl.sqrt(var + eps)\n mask = cols < N\n y = x * rstd\n tl.store(Y + cols, y, mask=mask)\n\ndef _l2_norm_fwd(\n x, eps=1e-6\n):\n x_shape_og = x.shape\n x = x.reshape(-1, x.shape[-1])\n if x.stride(-1) != 1:\n x = x.contiguous()\n y = torch.empty_like(x)\n N = x.shape[-1]\n M = x.shape[0]\n MAX_FUSED_SIZE = 65536 // x.element_size()\n BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))\n if N > BLOCK_N:\n raise RuntimeError(\n \"This layer norm doesn't support feature dim >= 64KB.\")\n with torch.cuda.device(x.device.index):\n _l2_norm_fwd_1pass_kernel[(M,)](\n x,\n y,\n x.stride(0),\n N,\n eps,\n BLOCK_N,\n )\n return y.reshape(x_shape_og)\n\n\n\n\n", "file": "l2_norm_triton1.py", "difficulty": "2"}] \ No newline at end of file +[{"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The provided Triton operator code implements a matrix-vector multiplication kernel, `mv_kernel`. The kernel function multiplies a matrix `A` of size N x M with a vector `B` of size M, resulting in an output vector `C` of size N. The kernel is parameterized by constants `BLOCK_N` and `BLOCK_M` which determine the tile sizes for the computation, allowing for optimal parallel processing. Within `mv_kernel`, a loop iterates over the matrix A in blocks of `BLOCK_M` to load sub-matrices and the corresponding block from vector B, performing element-wise multiplication and accumulation in shared memory. The partial results are then reduced along the rows and stored in the output vector `C`. The `mv` function wraps this kernel execution for launching the kernel with calculated grid dimensions.\n ", "input": "", "output": "import logging\n\nimport torch\nimport triton\nimport triton.language as tl\n\n\n@triton.autotune(\n configs=[\n triton.Config({\"BLOCK_M\": m, \"BLOCK_N\": n}, num_stages=s, num_warps=w)\n for m in [32, 64, 128]\n for n in [1, 2, 4, 8]\n for s in [3, 4]\n for w in [4, 8]\n ],\n key=[\"M\", \"N\"],\n)\n@triton.jit\ndef mv_kernel(\n A,\n B,\n C,\n N,\n M,\n stride_an,\n stride_am,\n stride_bm,\n stride_cn,\n BLOCK_N: tl.constexpr,\n BLOCK_M: tl.constexpr,\n):\n pid = tl.program_id(0)\n offset_n = pid * BLOCK_N + tl.arange(0, BLOCK_N)[:, None]\n offset_m = tl.arange(0, BLOCK_M)[None, :]\n n_mask = offset_n < N\n A_ptrs = A + offset_n * stride_an + offset_m * stride_am\n B_ptrs = B + offset_m * stride_bm\n acc = tl.zeros((BLOCK_N, BLOCK_M), dtype=tl.float32)\n for m in range(0, M, BLOCK_M):\n m_mask = m + offset_m < M\n a = tl.load(A_ptrs, mask=n_mask & m_mask, other=0.0).to(tl.float32)\n b = tl.load(B_ptrs, mask=m_mask, other=0.0).to(tl.float32)\n acc += a * b\n A_ptrs += BLOCK_M * stride_am\n B_ptrs += BLOCK_M * stride_bm\n\n acc = tl.sum(acc, axis=1)\n C_ptrs = C + offset_n * stride_cn\n tl.store(C_ptrs, acc[:, None], mask=n_mask)\n\n\ndef mv(inp, vec):\n logging.debug(\"GEMS MV\")\n assert inp.shape[1] == vec.shape[0], \"incompatible dimensions\"\n N, M = inp.shape\n out = torch.empty((N,), device=inp.device, dtype=inp.dtype)\n grid = lambda META: (triton.cdiv(N, META[\"BLOCK_N\"]),)\n with torch.cuda.device(inp.device):\n mv_kernel[grid](\n inp,\n vec,\n out,\n N,\n M,\n inp.stride(0),\n inp.stride(1),\n vec.stride(0),\n out.stride(0),\n )\n return out\n\n\n\n\n", "file": "matrix_vector_multip.py", "difficulty": "4"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The provided Triton kernel, `matmul_kernel`, is a specialized GPU matrix multiplication operation. \n It employs a blocked tiling strategy for efficient computation of the result matrix `c` from input matrices `a` and `b`. \n Within this kernel, operations are parallelized across blocks defined by BLOCK_SIZE_M, BLOCK_SIZE_N, and BLOCK_SIZE_K. \n These blocks allow the kernel to load sub-matrices, perform computations, and manage memory more efficiently.\n\n The kernel begins by computing indices for thread execution, segmenting the operation across various program IDs derived from the grid dimensions. \n For each thread block, it computes offsets `offs_am`, `offs_bn`, and `offs_k` to read data from the input matrices.\n\n In a loop iterating over slices of the K dimension, sub-matrices are loaded using `tl.load` with masks to handle boundary conditions. \n These matrices are then multiplied using `tl.dot`, accumulating results in a local accumulator. \n Memory access patterns are optimized using `tl.max_contiguous` and `tl.multiple_of` to align data in cache-friendly ways.\n\n The function finally writes the accumulated results to the output matrix `c`, with care taken to respect bounds and using conditional storage via `tl.store`.\n\n The `matmul` function wraps this kernel, preparing inputs and meta-parameters based on the matrix data types and dimensions. \n It enforces input compatibility, establishes execution grid dimensions, and sets device memory for output. \n Configuration parameters such as BLOCK_SIZE_M, num_stages, and num_warps are determined per data type, \n ensuring optimal kernel execution tailored for either float16 or Triton's experimental float8 types.\n ", "input": "", "output": "\nimport torch\nimport triton\nimport triton.language as tl\n\n\ndef _matmul_launch_metadata(grid, kernel, args):\n ret = {}\n M, N, K = args[\"M\"], args[\"N\"], args[\"K\"]\n ret[\"name\"] = f\"{kernel.name} [M={M}, N={N}, K={K}]\"\n if \"c_ptr\" in args:\n bytes_per_elem = args[\"c_ptr\"].element_size()\n else:\n bytes_per_elem = 1 if args[\"FP8_OUTPUT\"] else 2\n ret[f\"flops{bytes_per_elem * 8}\"] = 2. * M * N * K\n ret[\"bytes\"] = bytes_per_elem * (M * K + N * K + M * N)\n return ret\n\n\n@triton.jit(launch_metadata=_matmul_launch_metadata)\ndef matmul_kernel(a_ptr, b_ptr, c_ptr, #\n M, N, K, #\n stride_am, stride_ak, #\n stride_bk, stride_bn, #\n stride_cm, stride_cn, #\n BLOCK_SIZE_M: tl.constexpr, #\n BLOCK_SIZE_N: tl.constexpr, #\n BLOCK_SIZE_K: tl.constexpr, #\n GROUP_SIZE_M: tl.constexpr, #\n ):\n pid = tl.program_id(axis=0)\n num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)\n num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)\n num_pid_in_group = GROUP_SIZE_M * num_pid_n\n group_id = pid // num_pid_in_group\n first_pid_m = group_id * GROUP_SIZE_M\n group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)\n pid_m = first_pid_m + (pid % group_size_m)\n pid_n = (pid % num_pid_in_group) // group_size_m\n\n start_m = pid_m * BLOCK_SIZE_M\n start_n = pid_n * BLOCK_SIZE_N\n\n offs_am = start_m + tl.arange(0, BLOCK_SIZE_M)\n offs_bn = start_n + tl.arange(0, BLOCK_SIZE_N)\n offs_am = tl.where(offs_am < M, offs_am, 0)\n offs_bn = tl.where(offs_bn < N, offs_bn, 0)\n\n offs_am = tl.max_contiguous(tl.multiple_of(offs_am, BLOCK_SIZE_M), BLOCK_SIZE_M)\n offs_bn = tl.max_contiguous(tl.multiple_of(offs_bn, BLOCK_SIZE_N), BLOCK_SIZE_N)\n offs_k = tl.arange(0, BLOCK_SIZE_K)\n a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)\n b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)\n\n accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)\n\n for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):\n a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)\n b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)\n accumulator = tl.dot(a, b, accumulator)\n a_ptrs += BLOCK_SIZE_K * stride_ak\n b_ptrs += BLOCK_SIZE_K * stride_bk\n\n if (c_ptr.dtype.element_ty == tl.float8e4nv):\n c = accumulator.to(tl.float8e4nv)\n else:\n c = accumulator.to(tl.float16)\n\n offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)\n offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]\n c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)\n tl.store(c_ptrs, c, mask=c_mask)\n\n\ndef matmul(a, b):\n configs = {\n torch.float8_e4m3fn: {\n \"BLOCK_SIZE_M\": 128, \"BLOCK_SIZE_N\": 256, \"BLOCK_SIZE_K\": 128, \"GROUP_SIZE_M\": 8, \"num_stages\": 4,\n \"num_warps\": 8\n }, torch.float16: {\n \"BLOCK_SIZE_M\": 128, \"BLOCK_SIZE_N\": 256, \"BLOCK_SIZE_K\": 64, \"GROUP_SIZE_M\": 8, \"num_stages\": 3,\n \"num_warps\": 8\n }\n }\n # Check constraints.\n assert a.shape[1] == b.shape[0], \"Incompatible dimensions\"\n assert a.dtype == b.dtype, \"Incompatible dtypes\"\n M, K = a.shape\n K, N = b.shape\n dtype = a.dtype\n\n c = torch.empty((M, N), device=a.device, dtype=dtype)\n # 1D launch kernel where each block gets its own program.\n grid = lambda META: (triton.cdiv(M, META[\"BLOCK_SIZE_M\"]) * triton.cdiv(N, META[\"BLOCK_SIZE_N\"]), )\n matmul_kernel[grid](\n a, b, c, #\n M, N, K, #\n a.stride(0), a.stride(1), #\n b.stride(0), b.stride(1), #\n c.stride(0), c.stride(1), #\n BLOCK_SIZE_M=configs[dtype][\"BLOCK_SIZE_M\"], #\n BLOCK_SIZE_N=configs[dtype][\"BLOCK_SIZE_N\"], #\n BLOCK_SIZE_K=configs[dtype][\"BLOCK_SIZE_K\"], #\n GROUP_SIZE_M=configs[dtype][\"GROUP_SIZE_M\"], #\n num_stages=configs[dtype][\"num_stages\"], #\n num_warps=configs[dtype][\"num_warps\"], #\n )\n return c\n\n\n\n\n", "file": "triton_matmul.py", "difficulty": "3"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The Triton-accelerated function embedding_kernel is specialized for extracting and storing embedding vectors from a weight matrix for a sequence of token IDs. It uses program IDs to determine processing offsets and handles iteration over sequences with BLOCK_N and BLOCK_NN stride sizes. For each sequence, it computes token IDs and uses masks to ensure only valid data is loaded and processed. The weight matrix is addressed using a combination of token IDs and dimension offsets, facilitated by the stride of the weight tensor. The processed vectors are then stored into the 'out' tensor using calculated strides and masks, ensuring each output sequence position receives the correct embedding vector. The wrapping function, embedding, configures and invokes the kernel with appropriate grid settings, aligning BLOCK_DMODEL to the next power of two based on weight dimensions and leveraging constant memory settings to optimize the embedding extraction process.\n ", "input": "", "output": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef embedding_kernel(\n weight,\n input_ids,\n out,\n vob_start_id,\n vob_end_id,\n stride_weight_seq,\n stride_out_seq,\n n_ctx,\n hiden_size: tl.constexpr,\n BLOCK_DMODEL: tl.constexpr,\n BLOCK_N: tl.constexpr,\n BLOCK_NN: tl.constexpr,\n):\n start_n = tl.program_id(0) * BLOCK_N\n\n offs_nn = start_n + tl.arange(0, BLOCK_NN)\n offs_d = tl.arange(0, BLOCK_DMODEL)\n\n for start_nn in range(0, BLOCK_N, BLOCK_NN):\n start_nn = tl.multiple_of(start_nn, BLOCK_NN)\n offs_seq = start_nn + offs_nn\n n_ctx_mask = offs_seq < n_ctx\n token_ids = tl.load(input_ids + offs_seq, mask=n_ctx_mask, other=vob_end_id)\n id_mask = (token_ids >= vob_start_id) & (token_ids < vob_end_id)\n token_ids = token_ids - vob_start_id\n dim_mask = offs_d < hiden_size\n load_mask = id_mask[:, None] & dim_mask[None, :]\n store_mask = n_ctx_mask[:, None] & dim_mask[None, :]\n vecs = tl.load(weight + token_ids[:, None] * stride_weight_seq + offs_d[None, :], mask=load_mask, other=0.0)\n tl.store(out + offs_seq[:, None] * stride_out_seq + offs_d[None, :], vecs, mask=store_mask)\n\n@torch.no_grad()\ndef embedding(input_ids, weight: torch.Tensor, vob_start_id, vob_end_id, out: torch.Tensor):\n BLOCK_N = 64\n BLOCK_NN = 1\n BLOCK_DMODEL = triton.next_power_of_2(weight.shape[1])\n n_ctx = input_ids.shape[0]\n\n grid = (triton.cdiv(n_ctx, BLOCK_N), 1, 1)\n\n embedding_kernel[grid](\n weight,\n input_ids,\n out,\n vob_start_id,\n vob_end_id,\n weight.stride(0),\n out.stride(0),\n n_ctx=n_ctx,\n hiden_size=weight.shape[1],\n BLOCK_DMODEL=BLOCK_DMODEL,\n BLOCK_N=BLOCK_N,\n BLOCK_NN=BLOCK_NN,\n num_warps=1,\n num_stages=1,\n )\n\n\n\n\n", "file": "embedding_triton_kernel.py", "difficulty": "3"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The code defines a Triton-based kernel for matrix multiplication of INT4 quantized weights and provides Python functions to handle the quantization and dequantization processes. \n The 'matmul_kernel' function is a Triton kernel using @triton.jit which performs matrix multiplication. It processes the input matrices in tiles defined by BLOCK_SIZE_M, BLOCK_SIZE_N, and BLOCK_SIZE_K, and uses a loop to iterate over the K dimension to accumulate results in fp32 precision, subsequently stored in the output buffer, potentially employing atomic add for SPLIT_K > 1.\n The kernel is set up with a wide variety of configurations, allowing Triton's autotuning capabilities to select optimal parameters based on matrix dimensions M, N, and K. It utilizes quantized INT4 weights, reconstructing them using scales and zero points to compute matrix products accurately. \n The Python function 'matmul_dequantize_int4_s2' serves as an interface to this kernel, preparing data and launching the computation on specified grid dimensions.\n The function 'quantize_int4' converts a floating-point weight matrix into INT4 format, organizing the data by packing 8 INT4 values into one INT32 and calculating scaling factors and zero points for each group of elements. \n The helper function 'unpack_int4' is intended for testing; it unpacks the INT4 matrix back into a floating-point format to verify the quantization process.\n ", "input": "", "output": "import time\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.autotune(\n\tconfigs=[\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n\t \n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n\t\ttriton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n\t\t\n ],\n\tkey=['M', 'N', 'K'],\n reset_to_zero=['c_ptr']\n)\n@triton.jit\ndef matmul_kernel(\n a_ptr, b_ptr, c_ptr,\n bs_ptr, bzp_ptr,\n M, N, K,\n stride_am, stride_ak,\n stride_bk, stride_bn,\n stride_cm, stride_cn,\n stride_bsk, stride_bsn,\n stride_bzpk, stride_bzpn,\n group_size,\n BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,\n GROUP_SIZE_M: tl.constexpr, SPLIT_K: tl.constexpr\n ):\n \"\"\"\n assert K % (BLOCK_SIZE_K * SPLIT_K) == 0\n \"\"\"\n pid = tl.program_id(axis=0)\n pid_sp_k = tl.program_id(axis=1)\n num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)\n num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)\n num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)\n num_pid_in_group = GROUP_SIZE_M * num_pid_n\n group_id = pid // num_pid_in_group\n first_pid_m = group_id * GROUP_SIZE_M\n group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)\n pid_m = first_pid_m + (pid % group_size_m)\n pid_n = (pid % num_pid_in_group) // group_size_m \n offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M\n offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N\n offs_k = pid_sp_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)\n\n # [BLOCK_M, BLOCK_K]\n a_ptrs = a_ptr + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak\n # [BLOCK_K, BLOCK_N] but repeated 8 times in N\n b_ptrs = b_ptr + (offs_k[:, None] // 8) * stride_bk + offs_bn[None, :] * stride_bn\n # tl.static_print(\"shape\", a_ptrs, b_ptrs, bs_ptrs, bzp_ptrs)\n # -----------------------------------------------------------\n # Iterate to compute a block of the C matrix.\n # We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block\n # of fp32 values for higher accuracy.\n # `accumulator` will be converted back to fp16 after the loop.\n accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)\n for k in range(0, tl.cdiv(K, BLOCK_SIZE_K * SPLIT_K)):\n # Load the next block of A and B.\n # [BLOCK_K, BLOCK_N] but repeated group_size times in K \n bs_ptrs = bs_ptr + ((offs_k[:, None] + k * BLOCK_SIZE_K * SPLIT_K) // group_size) * stride_bsk \\\n + offs_bn[None, :] * stride_bsn\n # [BLOCK_K, BLOCK_N] but repeated in K and N\n bzp_ptrs = bzp_ptr + ((offs_k[:, None] + k * BLOCK_SIZE_K * SPLIT_K) // group_size) * stride_bzpk \\\n + (offs_bn[None, :] // 8) * stride_bzpn\n b_shift_bits = (offs_k[:, None] % 8) * 4 # assert BLOCK_SIZE_K % 8 == 0\n bzp_shift_bits = (offs_bn[None, :] % 8) * 4\n a = tl.load(a_ptrs)\n b = tl.load(b_ptrs)\n bs = tl.load(bs_ptrs)\n bzp = tl.load(bzp_ptrs)\n # We accumulate along the K dimension.\n int_b = (b >> b_shift_bits) & 0xF\n int_bzp = (bzp >> bzp_shift_bits) & 0xF\n b = ((int_b - int_bzp) * bs).to(a.dtype)\n accumulator += tl.dot(a, b.to(a.dtype))\n # Advance the ptrs to the next K block.\n a_ptrs += BLOCK_SIZE_K * SPLIT_K * stride_ak\n b_ptrs += (BLOCK_SIZE_K * SPLIT_K * stride_bk // 8) # assert BLOCK_SIZE_K % 8 == 0\n # You can fuse arbitrary activation functions here\n # while the accumulator is still in FP32!\n c = accumulator.to(c_ptr.dtype.element_ty)\n # -----------------------------------------------------------\n # Write back the block of the output matrix C with masks.\n offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)\n offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]\n c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)\n if SPLIT_K == 1:\n tl.store(c_ptrs, c, mask=c_mask)\n else:\n tl.atomic_add(c_ptrs, c, mask=c_mask)\n\n\ndef matmul_dequantize_int4_s2(x: torch.FloatTensor, qweight: torch.IntTensor, scales: torch.FloatTensor, qzeros: torch.IntTensor, group_size: int = 128, output=None) -> torch.FloatTensor:\n \"\"\"\n \"\"\"\n assert x.is_contiguous(), \"A must be contiguous\"\n assert qweight.is_contiguous(), \"B must be contiguous\" \n M, K = x.shape\n N = scales.shape[1]\n if output is None:\n output = torch.zeros((M, N), device=x.device, dtype=x.dtype) \n grid = lambda META: (\n triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),\n META['SPLIT_K'],\n )\n matmul_kernel[grid](\n x, qweight, output,\n scales, qzeros,\n M, N, K,\n x.stride(0), x.stride(1),\n qweight.stride(0), qweight.stride(1),\n output.stride(0), output.stride(1),\n scales.stride(0), scales.stride(1),\n qzeros.stride(0), qzeros.stride(1),\n group_size,\n )\n return output\n\ndef quantize_int4(weight, group_size=128, tp_rank=0):\n # Weight shape: [H1 // 8, H2]\n # Scale shape: [H1 // group_size, H2]\n # zero_pint shape: [H1 // group_size, H2 // 8]\n\n weight = weight.transpose(1, 0)\n h1, h2 = weight.shape\n assert h1 % 8 == 0 and h2 % 8 == 0, \"H1 {} H2 {}\".format(h1, h2)\n assert h2 % group_size == 0, \"H1 {} H2 {}\".format(h1, h2)\n weight = weight.contiguous().view(-1, group_size).cuda(tp_rank)\n weight_max = weight.amax(-1, keepdim=True)\n weight_max = torch.where(weight_max < 0, 0, weight_max)\n weight_min = weight.amin(-1, keepdim=True)\n weight_min = torch.where(weight_min > 0, 0, weight_min)\n weight_range = weight_max - weight_min \n scale = weight_range / (2 ** 4 - 1)\n zero_point = (-weight_min / scale).round().clamp(0, 15).to(torch.int32)\n weight = (weight / scale + zero_point).round().clamp(0, 15).to(torch.int32).view(h1, h2)\n int_weight = torch.empty(h1, h2 // 8).to(torch.int32).to(weight.device)\n int_zero_point = torch.zeros(h1 // 8, h2 // group_size).to(torch.int32).to(weight.device)\n zero_point = zero_point.view(h1, -1)\n scale = scale.view(h1, -1)\n # pack 8 int4 in an int32 number.\n # Weight pack in row.\n for pack in range(0, h2, 8):\n for i in range(8):\n int_weight[:, pack // 8] += weight[:, pack + i] << (i * 4)\n # zero point pack in col.\n for pack in range(0, h1, 8):\n for i in range(8):\n int_zero_point[pack // 8, :] += zero_point[pack + i, :] << (i * 4)\n '''\n fp_weight = torch.zeros(h1, h2).half().to(weight.device)\n for pack in range(0, h1 // 8):\n for i in range(8):\n fp_weight[pack * 8 + i, :] = \\\n ((int_weight[pack, :] << (28 - i * 4) >> 28) + 16) % 16\n print((fp_weight - weight).abs().sum())\n\n fp_zp = torch.zeros(zero_point.shape).half().to(zero_point.device)\n for pack in range(0, h1 // 8):\n for i in range(8):\n fp_zp[pack * 8 + i, :] = \\\n (int_zero_point[pack, :] >> (i * 4)) & 15\n\n print((fp_zp - zero_point).abs().sum())\n '''\n weight = None\n return int_weight.transpose(1, 0).contiguous(), scale.transpose(1, 0).contiguous(), int_zero_point.transpose(1, 0).contiguous(), group_size\n\n\ndef unpack_int4(weight, scale, zp):\n \"\"\"\n Test function to verify quantize int4 is correct.\n Will not be used in model inference.\n \"\"\"\n weight = weight.transpose(1, 0)\n scale = scale.transpose(1, 0)\n zp = zp.transpose(1, 0)\n h1, h2 = weight.shape\n group_size = h2 * 8 // scale.shape[1]\n group_num = scale.shape[1]\n fp_weight = torch.zeros(h1, h2 * 8).half().to(weight.device)\n fp_zero_point = torch.zeros(h1, group_num).to(weight.device)\n for pack in range(0, h2):\n for i in range(8):\n fp_weight[:, pack * 8 + i] = (weight[:, pack] >> (i * 4)) & 0xF\n for pack in range(0, h1 // 8):\n for i in range(8):\n fp_zero_point[pack * 8 + i, :] = (zp[pack, :] >> (i * 4)) & 0xF\n for g in range(group_num):\n fp_weight[:, g * group_size:(g + 1) * group_size] = (fp_weight[:, g * group_size:(g + 1) * group_size] - \\\n fp_zero_point[:, g].unsqueeze(1)) * scale[:, g].unsqueeze(1)\n return fp_weight.transpose(1, 0)\n\n\n\n", "file": "int4_matmul.py", "difficulty": "5"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The `_fwd_kernel_flash_decode_stage2` Triton kernel is a parallel computation designed for processing sequences in a neural network context, specifically dealing with batches, heads, and sequence blocks. This kernel receives several inputs: `B_Seqlen`, `Mid_O`, `Mid_O_LogExpSum`, and `Out`, along with strides for indexing. `B_Seqlen` contains sequence lengths per batch, `Mid_O` contains intermediate outputs, `Mid_O_LogExpSum` holds log-exp sum values, and `Out` will store the final output. The kernel operates over a 2D grid defined by batch size and head count (`grid = (batch, head_num)`), with constants `BLOCK_SEQ` and `BLOCK_DMODEL` indicating sequence block size and dimension alignment respectively.\n\n The kernel function operates as follows:\n - Identifies the current batch and head using `tl.program_id`.\n - Initializes accumulators: `sum_exp`, `max_logic`, and `acc` to accumulate exponential logic and values.\n - Loads the current sequence length and calculates the number of sequence blocks (`block_n_size`).\n - Iterates over each block, where:\n - It loads values (`tv`) from `Mid_O` and logic sums (`tlogic`) from `Mid_O_LogExpSum`.\n - Computes the maximum logic value across blocks and scales previous accumulations.\n - Updates the accumulators by computing the exponential of adjusted logic values and scaling/accumulating.\n - Stores the final normalized result into `Out`, scaling accumulated values by the sum of exponentials.\n\n The `flash_decode_stage2` function sets up and invokes this kernel, determining dimensions and grid setup based on input tensor shapes. It ensures efficient computation by using Triton's parallel execution framework, specifying warp and stage numbers.\n ", "input": "", "output": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef _fwd_kernel_flash_decode_stage2(\n B_Seqlen,\n Mid_O, # [batch, head, seq_block_num, head_dim]\n Mid_O_LogExpSum, # [batch, head, seq_block_num]\n Out, # [batch, head, head_dim]\n stride_mid_ob,\n stride_mid_oh,\n stride_mid_os,\n stride_mid_od,\n stride_mid_o_eb,\n stride_mid_o_eh,\n stride_mid_o_es,\n stride_obs,\n stride_oh,\n stride_od,\n head_dim,\n BLOCK_SEQ: tl.constexpr,\n BLOCK_DMODEL: tl.constexpr,\n):\n cur_batch = tl.program_id(0)\n cur_head = tl.program_id(1)\n\n offs_d = tl.arange(0, BLOCK_DMODEL)\n cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)\n\n block_n_size = tl.where(cur_batch_seq_len <= 0, 0, cur_batch_seq_len + BLOCK_SEQ - 1) // BLOCK_SEQ\n\n sum_exp = 0.0\n max_logic = -float(\"inf\")\n acc = tl.zeros([BLOCK_DMODEL], dtype=tl.float32)\n\n offs_v = cur_batch * stride_mid_ob + cur_head * stride_mid_oh + offs_d\n offs_logic = cur_batch * stride_mid_o_eb + cur_head * stride_mid_o_eh\n for block_seq_n in range(0, block_n_size, 1):\n tv = tl.load(Mid_O + offs_v + block_seq_n * stride_mid_os, mask=offs_d < head_dim, other=0.0)\n tlogic = tl.load(Mid_O_LogExpSum + offs_logic + block_seq_n)\n new_max_logic = tl.maximum(tlogic, max_logic)\n\n old_scale = tl.exp(max_logic - new_max_logic)\n acc *= old_scale\n exp_logic = tl.exp(tlogic - new_max_logic)\n acc += exp_logic * tv\n sum_exp = sum_exp * old_scale + exp_logic\n max_logic = new_max_logic\n\n tl.store(Out + cur_batch * stride_obs + cur_head * stride_oh + offs_d, acc / sum_exp, mask=offs_d < head_dim)\n return\n\n@torch.no_grad()\ndef flash_decode_stage2(mid_out, mid_out_logexpsum, B_Seqlen, Out, block_seq):\n Lk = mid_out.shape[-1]\n head_dim = Lk\n batch, head_num = mid_out.shape[0], mid_out.shape[1]\n BLOCK_DMODEL = triton.next_power_of_2(head_dim)\n grid = (batch, head_num)\n\n _fwd_kernel_flash_decode_stage2[grid](\n B_Seqlen,\n mid_out,\n mid_out_logexpsum,\n Out,\n mid_out.stride(0),\n mid_out.stride(1),\n mid_out.stride(2),\n mid_out.stride(3),\n mid_out_logexpsum.stride(0),\n mid_out_logexpsum.stride(1),\n mid_out_logexpsum.stride(2),\n Out.stride(0),\n Out.stride(1),\n Out.stride(2),\n head_dim,\n BLOCK_SEQ=block_seq,\n BLOCK_DMODEL=BLOCK_DMODEL,\n num_warps=4,\n num_stages=2,\n )\n return\n\n\n\n\n", "file": "flash_decode2_phi.py", "difficulty": "2"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\nThe Triton operator is defined to transpose a matrix using a kernel function and a wrapper function. The kernel function named 'kernel' is decorated with '@triton.jit' for just-in-time compilation and performs matrix transposition by directly manipulating pointers based on the given strides and dimensions. It accepts input parameters such as a matrix 'M', an output buffer 'Out', the strides of 'M' and 'Out', and the dimensions 'SIZE_M' and 'D_HEAD'. The kernel computes the pointers for elements of 'M' using 'matrix_stridex' and 'matrix_stridey', and for 'Out' using 'out_stridex' and 'out_stridey'. The transposition is achieved by loading elements from 'M' and storing them into 'Out' in a transposed layout. The wrapper function named 'wrapper' initializes 'matrix' with random float16 values and 'out' with zeros, both on CUDA. It defines the grid configuration as a tuple with a single element, then calls the kernel with these matrices and their properties. Finally, it returns the transposed matrix 'out'.\n ", "input": "", "output": "import torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef kernel(\n M,\n Out,\n matrix_stridex,\n matrix_stridey,\n out_stridex,\n out_stridey,\n SIZE_M: tl.constexpr,\n D_HEAD: tl.constexpr,\n):\n size_m_arange = tl.arange(0, SIZE_M)\n d_head_arange = tl.arange(0, D_HEAD)\n # transpose\n matrix_ptr = M + d_head_arange[None, :] * matrix_stridey + size_m_arange[:, None] * matrix_stridex\n out_ptr = Out + d_head_arange[None, :] * out_stridex + size_m_arange[:, None] * out_stridey\n matrix = tl.load(matrix_ptr)\n tl.store(out_ptr, matrix)\n\ndef wrapper(size_m, d_head):\n matrix = torch.randn((size_m, d_head), dtype=torch.float16, device=\"cuda\")\n out = torch.zeros((d_head, size_m), dtype=torch.float16, device=\"cuda\")\n\n grid = (1,)\n kernel[grid](\n matrix,\n out,\n *matrix.stride(),\n *out.stride(),\n size_m,\n d_head,\n )\n return out\n\n\n\n", "file": "matrix_transpose.py", "difficulty": "2"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The `rotary_kernel` function is a Triton kernel that performs rotary position encoding on a tensor `X` using precomputed cosine (`COS`) and sine (`SIN`) matrices. It modifies or populates the output tensor `OUT` with the transformed data. The kernel accommodates both fixed and variable sequence lengths, controlled by the presence of `CU_SEQLENS`. The kernel handles interleaved and non-interleaved formats and allows for in-place transformations and conjugate computations if specified.\n\n The kernel operates in a three-dimensional grid, processing batches (`pid_batch`), heads (`pid_head`), and sequences (`pid_m`). It calculates transformations by loading blocks of data and applying rotary transformations based on cosine and sine values. The key operations are tailored based on whether the data is interleaved or not, with conditional handling for conjugation using `CONJUGATE`.\n\n The `apply_rotary` function acts as a high-level interface to the Triton kernel. It accepts the input tensor `x`, cosine and sine matrices, sequence length offsets, and optional cumulative sequence lengths (`cu_seqlens`). The function determines the execution grid and block sizes, aligning them with the input data shape and configuration. It initializes an output tensor, copying non-rotary parts of `x` if required. The function ensures that the kernel is called with appropriate arguments, matching the shape and type expectations set within the kernel logic. This design allows for efficient rotary transformations in transformer architectures.\n ", "input": "", "output": "from typing import Optional, Union\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef rotary_kernel(\n OUT, X, COS, SIN, CU_SEQLENS, SEQLEN_OFFSETS, seqlen, nheads, rotary_dim, seqlen_ro,\n CACHE_KEY_SEQLEN, stride_out_batch, stride_out_seqlen, stride_out_nheads,\n stride_out_headdim, stride_x_batch, stride_x_seqlen, stride_x_nheads,\n stride_x_headdim, BLOCK_K: tl.constexpr, IS_SEQLEN_OFFSETS_TENSOR: tl.constexpr,\n IS_VARLEN: tl.constexpr, INTERLEAVED: tl.constexpr, CONJUGATE: tl.constexpr, BLOCK_M: tl.constexpr,\n):\n pid_m = tl.program_id(axis=0)\n pid_batch = tl.program_id(axis=1)\n pid_head = tl.program_id(axis=2)\n rotary_dim_half = rotary_dim // 2\n\n if not IS_VARLEN:\n X = X + pid_batch * stride_x_batch + pid_head * stride_x_nheads\n OUT = OUT + pid_batch * stride_out_batch + pid_head * stride_out_nheads\n else:\n start_idx = tl.load(CU_SEQLENS + pid_batch)\n seqlen = tl.load(CU_SEQLENS + pid_batch + 1) - start_idx\n X = X + start_idx * stride_x_seqlen + pid_head * stride_x_nheads\n OUT = OUT + start_idx * stride_out_seqlen + pid_head * stride_out_nheads\n\n if pid_m * BLOCK_M >= seqlen:\n return\n rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)\n if not IS_SEQLEN_OFFSETS_TENSOR:\n rm_cs = rm + SEQLEN_OFFSETS\n else:\n rm_cs = rm + tl.load(SEQLEN_OFFSETS + pid_batch)\n rk = tl.arange(0, BLOCK_K)\n rk_half = tl.arange(0, BLOCK_K // 2)\n\n if not INTERLEAVED:\n X = X + (rm[:, None] * stride_x_seqlen + rk_half[None, :] * stride_x_headdim)\n COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :])\n SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :])\n cos = tl.load(COS, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=1.0).to(tl.float32)\n sin = tl.load(SIN, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=0.0).to(tl.float32)\n x0 = tl.load(X, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), other=0.0).to(tl.float32)\n x1 = tl.load(X + rotary_dim_half * stride_x_headdim, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), other=0.0).to(tl.float32)\n if CONJUGATE:\n sin = -sin\n o0 = x0 * cos - x1 * sin\n o1 = x0 * sin + x1 * cos\n OUT = OUT + (rm[:, None] * stride_out_seqlen + rk_half[None, :] * stride_out_headdim)\n tl.store(OUT, o0, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half))\n tl.store(OUT + rotary_dim_half * stride_out_headdim, o1, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half))\n else:\n rk_swap = rk + ((rk + 1) % 2) * 2 - 1\n rk_repeat = tl.arange(0, BLOCK_K) // 2\n X0 = X + (rm[:, None] * stride_x_seqlen + rk[None, :] * stride_x_headdim)\n X1 = X + (rm[:, None] * stride_x_seqlen + rk_swap[None, :] * stride_x_headdim)\n COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :])\n SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :])\n cos = tl.load(COS, mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half), other=1.0).to(tl.float32)\n sin = tl.load(SIN, mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half), other=0.0).to(tl.float32)\n x0 = tl.load(X0, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim), other=0.0).to(tl.float32)\n x1 = tl.load(X1, mask=(rm[:, None] < seqlen) & (rk_swap[None, :] < rotary_dim), other=0.0).to(tl.float32)\n if CONJUGATE:\n sin = -sin\n x0_cos = x0 * cos\n x1_sin = x1 * sin\n out = tl.where(rk[None, :] % 2 == 0, x0_cos - x1_sin, x0_cos + x1_sin)\n OUT = OUT + (rm[:, None] * stride_out_seqlen + rk[None, :] * stride_out_headdim)\n tl.store(OUT, out, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim))\n\ndef apply_rotary(\n x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, seqlen_offsets: Union[int, torch.Tensor] = 0,\n cu_seqlens: Optional[torch.Tensor] = None, max_seqlen: Optional[int] = None,\n interleaved=False, inplace=False, conjugate=False\n) -> torch.Tensor:\n is_varlen = cu_seqlens is not None\n if not is_varlen:\n batch, seqlen, nheads, headdim = x.shape\n else:\n total_seqlen, nheads, headdim = x.shape\n batch = cu_seqlens.shape[0] - 1\n seqlen = max_seqlen\n seqlen_ro, rotary_dim = cos.shape\n rotary_dim *= 2\n\n cos, sin = cos.contiguous(), sin.contiguous()\n if isinstance(seqlen_offsets, torch.Tensor):\n seqlen_offsets = seqlen_offsets.contiguous()\n else:\n seqlen_offsets += seqlen\n\n output = torch.empty_like(x) if not inplace else x\n if rotary_dim < headdim and not inplace:\n output[..., rotary_dim:].copy_(x[..., rotary_dim:])\n\n BLOCK_K = 32 if rotary_dim <= 32 else (64 if rotary_dim <= 64 else (128 if rotary_dim <= 128 else 256))\n grid = lambda META: (triton.cdiv(seqlen, META[\"BLOCK_M\"]), batch, nheads)\n BLOCK_M = 4 if interleaved else (8 if rotary_dim <= 64 else 4)\n\n with torch.cuda.device(x.device.index):\n rotary_kernel[grid](\n output, x, cos, sin, cu_seqlens, seqlen_offsets, seqlen, nheads, rotary_dim,\n seqlen_ro, seqlen // 128, output.stride(0) if not is_varlen else 0, output.stride(-3),\n output.stride(-2), output.stride(-1), x.stride(0) if not is_varlen else 0,\n x.stride(-3), x.stride(-2), x.stride(-1), BLOCK_K,\n isinstance(seqlen_offsets, torch.Tensor), is_varlen, interleaved, conjugate, BLOCK_M\n )\n return output\n\n\n\n\n", "file": "rotary_transform.py", "difficulty": "4"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The code implements a Triton kernel named `kernel_function`, which processes input data using block-wise operations. \n The kernel takes pointers to input and output data (`x_ptr` and `output_ptr`), the total number of elements to process (`n_elements`), and a constant block size (`BLOCK_SIZE`). \n Inside the kernel, each program instance calculates its starting point (`block_start`) and creates an `offsets` tensor for element indexing. \n A mask ensures operations only occur on valid indices within the input bounds. The kernel loads data from `x_ptr`, computes the sine using `tl.math.sin`, and stores the result in `output_ptr`. \n The `call_kernel` function prepares to execute the kernel by calculating the total number of elements (`n_elements`) and creates an output tensor. \n It defines a grid configuration function using lambda to handle thread block calculations based on `BLOCK_SIZE`, ensuring the entire input is processed. \n The kernel is then launched with the grid configuration, input, output, and element count.\n ", "input": "", "output": "\nimport torch\nimport triton\nimport triton.language as tl\n\n# Kernel function using Triton\n@triton.jit\ndef kernel_function(x_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):\n # x_ptr: pointer to input data\n # output_ptr: pointer to output data\n # n_elements: number of elements to process\n # BLOCK_SIZE: block size for Triton kernel\n pid = tl.program_id(0)\n block_start = pid * BLOCK_SIZE\n offsets = block_start + tl.arange(0, BLOCK_SIZE)\n mask = offsets < n_elements\n x = tl.load(x_ptr + offsets, mask=mask)\n output = tl.math.sin(x)\n tl.store(output_ptr + offsets, output, mask=mask)\n\n# Function to call the Triton kernel\ndef call_kernel(x):\n # x: input tensor\n n_elements = x.numel()\n output = torch.empty_like(x)\n grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n kernel_function[grid](x, output, n_elements, BLOCK_SIZE=1024)\n return output\n\n\n\n\n", "file": "sin_kernel.py", "difficulty": "1"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The Triton kernel `_l2_norm_bwd_kernel` performs a backward pass operation for L2 normalization on a per-row basis. It receives pointers to input `X`, output gradient `DY`, and calculates the input gradient `DX`. Each row of the input is accessed using the `stride_x_row`. `BLOCK_N` determines the number of elements processed per block, set based on maximum allowable fused size and next power of 2 of `N`. Within the kernel, it computes the variance of the input slice, uses it to compute the reciprocal of the standard deviation (`rstd`), and then calculates `dx` using the formula `dy * rstd - tl.sum(dy * x) * (1 / (var+eps)) * rstd * x`. The result is conditionally stored in `DX` using masks. The `_l2_norm_bwd` function orchestrates this process, ensuring input tensors `x` and `dy` are properly reshaped and their strides configured for contiguity if necessary. If `N` exceeds `BLOCK_N`, an error is raised to prevent excessive feature dimensions. Finally, the kernel is launched over `M` rows of the reshaped tensors, and the output `dx` is reshaped back to the original input shape.\n ", "input": "", "output": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef _l2_norm_bwd_kernel(\n X, # pointer to the input\n DY, # pointer to the output gradient\n DX, # pointer to the input gradient\n stride_x_row, # how much to increase the pointer when moving by 1 row\n N, # number of columns in X\n eps, # epsilon to avoid division by zero\n BLOCK_N: tl.constexpr,\n):\n row = tl.program_id(0)\n X += row * stride_x_row\n DX += row * stride_x_row\n DY += row * stride_x_row\n cols = tl.arange(0, BLOCK_N)\n x = tl.load(X + cols, mask=cols < N, other=0.0).to(tl.float32)\n x = tl.where(cols < N, x, 0.0)\n var = tl.sum(x * x) \n rstd = 1 / tl.sqrt(var + eps)\n mask = cols < N\n dy = tl.load(DY + cols, mask=cols < N, other=0.0).to(tl.float32)\n dy = tl.where(cols < N, dy, 0.0)\n dx = dy * rstd - tl.sum(dy * x) * (1 / (var+eps)) * rstd * x\n tl.store(DX + cols, dx, mask=mask)\n\ndef _l2_norm_bwd(\n x, dy, eps=1e-5,\n):\n x_shape_og = x.shape\n x = x.reshape(-1, dy.shape[-1])\n dy = dy.reshape(-1, dy.shape[-1])\n if dy.stride(-1) != 1:\n dy = dy.contiguous()\n dx = torch.empty_like(x)\n N = x.shape[-1]\n M = x.shape[0]\n MAX_FUSED_SIZE = 65536 // x.element_size()\n BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))\n if N > BLOCK_N:\n raise RuntimeError(\n \"This layer norm doesn't support feature dim >= 64KB.\")\n with torch.cuda.device(x.device.index):\n _l2_norm_bwd_kernel[(M,)](\n x,\n dy,\n dx,\n x.stride(0),\n N,\n eps,\n BLOCK_N,\n )\n return dx.reshape(x_shape_og)\n\n\n\n\n", "file": "l2_norm_bwd.py", "difficulty": "3"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The Triton kernel `_l2_norm_fwd_1pass_kernel` aims to perform L2 normalization on a 2D input tensor `X`. It processes each row separately using Triton's parallel execution model. The kernel expects pointers to `X` and `Y` along with the stride for rows (`stride_x_row`), number of columns in `X` (`N`), a small constant `eps` to prevent division by zero, and a compile-time constant `BLOCK_N`. The kernel computes L2 normalization by first loading a block of data from `X`, calculating the sum of squares for variance, and computing the reciprocal of the square root of the variance plus `eps` to get `rstd`. It then multiplies the input block by `rstd` to produce the normalized values, which are stored in `Y`.\n\n The Python function `_l2_norm_fwd` handles the setup and execution of the kernel. It first reshapes and possibly makes the input tensor `x` contiguous. It initializes an empty tensor `y` to store the output. The function calculates `BLOCK_N` based on `x`'s element size and ensures it doesn't exceed 64KB. If the feature dimension `N` is larger than `BLOCK_N`, it raises an error. The kernel is then launched with the total number of rows `M`, pointers to `x` and `y`, stride, number of columns, `eps`, and `BLOCK_N`. Finally, the function returns the normalized tensor reshaped to its original dimensions.\n ", "input": "", "output": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef _l2_norm_fwd_1pass_kernel(\n X, # pointer to the input\n Y, # pointer to the output\n stride_x_row, # how much to increase the pointer when moving by 1 row\n N, # number of columns in X\n eps, # epsilon to avoid division by zero\n BLOCK_N: tl.constexpr,\n):\n row = tl.program_id(0)\n X += row * stride_x_row\n Y += row * stride_x_row\n cols = tl.arange(0, BLOCK_N)\n x = tl.load(X + cols, mask=cols < N, other=0.0).to(tl.float32)\n xbar = tl.where(cols < N, x, 0.0)\n var = tl.sum(xbar * xbar, axis=0) \n rstd = 1 / tl.sqrt(var + eps)\n mask = cols < N\n y = x * rstd\n tl.store(Y + cols, y, mask=mask)\n\ndef _l2_norm_fwd(\n x, eps=1e-6\n):\n x_shape_og = x.shape\n x = x.reshape(-1, x.shape[-1])\n if x.stride(-1) != 1:\n x = x.contiguous()\n y = torch.empty_like(x)\n N = x.shape[-1]\n M = x.shape[0]\n MAX_FUSED_SIZE = 65536 // x.element_size()\n BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))\n if N > BLOCK_N:\n raise RuntimeError(\n \"This layer norm doesn't support feature dim >= 64KB.\")\n with torch.cuda.device(x.device.index):\n _l2_norm_fwd_1pass_kernel[(M,)](\n x,\n y,\n x.stride(0),\n N,\n eps,\n BLOCK_N,\n )\n return y.reshape(x_shape_og)\n\n\n\n\n", "file": "l2_norm_triton1.py", "difficulty": "2"}] \ No newline at end of file diff --git a/src/dataloaders/TritonBench.py b/src/dataloaders/TritonBench.py index b879acc..339c5cd 100644 --- a/src/dataloaders/TritonBench.py +++ b/src/dataloaders/TritonBench.py @@ -7,8 +7,8 @@ import signal from multiprocessing import Pool, Lock, Value from dataloaders.ProblemState import ProblemState -from dataloaders.TB_eval.utils import code_call_exec_success_allclose - +from dataloaders.TB_eval.utils import code_call_exec_success_allclose, process_code +from loguru import logger class TritonBench: @@ -45,6 +45,9 @@ def load_ps(self, path, target_kernels=None): instruction = line["instruction"] # label = line["output"] file = line["file"] + if target_kernels is not None and file not in target_kernels: + logger.info(f"skip {file} because it is not in target_kernels") + continue label = None path = os.path.join(self.py_folder, file) @@ -52,10 +55,18 @@ def load_ps(self, path, target_kernels=None): test_code = open(path, "r", encoding="utf-8").read().split("#"*146)[-1] assert "def test_" in test_code, "" + ref_code = None + if self.py_folder is not None: + path = os.path.join(self.py_folder, file) + assert os.path.exists(path), f"{path} not exist!" + ref_code = open(path, "r").read().split("#"*146)[0] + ref_code = process_code(ref_code) + problemstate = ProblemState(instruction=instruction, label=label, test_code=test_code, filename=file, + ref_code=ref_code, ) problem_states.append( diff --git a/src/main_reflexion_oneshot.py b/src/main_reflexion_oneshot.py index b5d4f9f..15240b1 100644 --- a/src/main_reflexion_oneshot.py +++ b/src/main_reflexion_oneshot.py @@ -4,7 +4,11 @@ from models.KimiK2 import KimiK2Model from dataloaders.TritonBench import TritonBench from args_config import load_config - +from loguru import logger +import sys, time +logger.remove() +logger.add(sys.stdout, level="INFO") +logger.add(f"logs/reflexion_oneshot_debug_{time.strftime('%Y%m%d_%H%M%S')}.log", level="DEBUG") def main(): args = load_config("configs/tritonbench_oneshot_config.yaml") diff --git a/src/models/KimiK2.py b/src/models/KimiK2.py index 9728de8..d12ffdc 100644 --- a/src/models/KimiK2.py +++ b/src/models/KimiK2.py @@ -2,10 +2,11 @@ from typing import List import openai from tenacity import retry, stop_after_attempt, wait_random_exponential - +from loguru import logger from models.Base import BaseModel + class KimiK2Model(BaseModel): def __init__(self, model_id="Kimi-K2-Instruct", @@ -35,6 +36,7 @@ def generate(self, presence_penalty=0, frequency_penalty=0, max_tokens=5000) -> str: + logger.debug(f"prompt: {messages}") response = self.client.chat.completions.create( model=self.model_id, messages=messages, @@ -45,7 +47,7 @@ def generate(self, if not response or not hasattr(response, 'choices') or len(response.choices) == 0: raise ValueError("No response choices returned from the API.") - + logger.debug(f"response: {response.choices[0].message.content}") return response.choices[0].message.content diff --git a/src/prompts/prompt_for_generation.py b/src/prompts/prompt_for_generation.py index 6d07461..9e1538b 100644 --- a/src/prompts/prompt_for_generation.py +++ b/src/prompts/prompt_for_generation.py @@ -49,6 +49,50 @@ **Generated AMD ROCm Compatible Triton Kernel Code:** """ +my_prompt = """ +{instruction} + +**CRITICAL FUNCTION INFORMATION:** +Based on analysis, the implementation requires these EXACT function signatures: +{function_signatures} + +**Output Requirements:** +1. **AMD Compatibility:** Generate code compatible with AMD GPUs and ROCm. **DO NOT use CUDA-specific features or functions (e.g., `tl.libdevice`).** +2. **Complete Code:** Generate a single, complete, and syntactically correct Python code block. +3. **Triton Kernel:** The core logic must be implemented within a Triton kernel function decorated with `@triton.jit`. +4. **Imports:** ALWAYS include necessary imports at the beginning: + ```python + import torch + import triton + import triton.language as tl + # import math # Only if standard math functions are truly needed outside the kernel + ``` + Include other imports *only if absolutely necessary*. +5. **Function Signature (CRITICAL):** + * Define EACH function with EXACTLY the signature shown above. + * DO NOT change parameter names, counts, or order. + * Ensure all parameters in function calls match their function definitions. + * **Type Hints:** Use PyTorch tensor type hints (e.g., `x: torch.Tensor`) for tensor arguments. **DO NOT use `tl.pointer`**. Use standard Python types (e.g., `int`, `float`) or `tl.constexpr` for others. + * **`constexpr`:** Use `tl.constexpr` **ONLY** for arguments that *must* be known at compile time, typically block sizes (like `BLOCK_SIZE`, `BLOCK_M`) or flags that change the kernel's structure (like `IS_EVEN_K`). Simple numerical values like `eps` or `dropout_p` are usually *not* `constexpr`. +6. **Data Types:** Be precise with data types inside the kernel (e.g., `tl.float16`, `tl.float32`, `tl.int32`). Ensure type compatibility. Assume input tensors might be `torch.float16` or `torch.float32` unless specified otherwise. Pay attention to potential type promotion/conversion needs (e.g., using `.to(tl.float32)` for accumulations). +7. **Triton Operations:** + * Use Triton language functions correctly (`tl.load`, `tl.store`, `tl.dot`, `tl.arange`, `tl.program_id`, `tl.where`, `tl.atomic_cas`, etc.). + * **Pointers & Masks:** Be extremely careful when constructing pointers using offsets and strides. Ensure masks in `tl.load`/`tl.store` are correctly computed and match pointer dimensions. Avoid `ValueError: Mask argument cannot be block type...` or `ValueError: Unsupported ptr type...`. + * **`tl.dot`:** Ensure inputs are 2D blocks and have compatible types (e.g., float16, bfloat16). Int32 is generally not supported directly as input. + * **`tl.arange`:** Arguments `start` and `end` **must be `tl.constexpr`**. + * **Math:** Use functions from `tl.math` where available (e.g., `tl.math.exp`, `tl.math.sqrt`). Check function existence; avoid assuming functions like `tanh` or `log1p` exist if they don't in `tl.math`. +8. **Triton Version:** Assume Triton version 3.1.0 or later. + +**FINAL VERIFICATION:** +Before completing, verify: +1. ALL functions defined in the code have EXACT signatures matching the required function signatures above. +2. ALL function calls exactly match their definitions in terms of parameter counts and names. +3. No functions are called without being defined. +4. No parameters are missing from your implementations. + +**Generated AMD ROCm Compatible Triton Kernel Code:** +""" + prompt_rocm = """ You are an expert Python programmer specializing in NVIDIA Triton kernels, specifically targeting **AMD GPUs using the ROCm environment**. Your task is to generate a Python code snippet containing a Triton kernel based on the following request: diff --git a/src/prompts/prompt_for_reflection.py b/src/prompts/prompt_for_reflection.py index fe3f936..c6026d4 100644 --- a/src/prompts/prompt_for_reflection.py +++ b/src/prompts/prompt_for_reflection.py @@ -22,6 +22,30 @@ """ +my_prompt = """ +You are an expert in writing Triton operators for efficient GPU programming. Analyze the failed test cases and provide insights +on why the solution failed and how it could be improved. Be specific about the issues found. + +**Original problem:** + +{problem} + +**Attempted solution:** + +{solution} + +**Test results:** + +{test_result} + +**Important Instructions:** +- Think before writing the reflection and no more explanation is required after the reflection. +- You should not suggest changes to the name of the function. +- generate the reflection wrapped in a code block with the tag `reflection`, e.g. +"```markdown```" + +""" + prompt_exe = """ You are an expert in writing Triton operators for efficient GPU programming. Analyze the failed test cases and provide insights on why the solution failed and how it could be improved. Be specific about the issues found. diff --git a/src/reflexion_oneshot_tritonbench_iter1_4.json b/src/reflexion_oneshot_tritonbench_iter1_4.json new file mode 100644 index 0000000..d844f80 --- /dev/null +++ b/src/reflexion_oneshot_tritonbench_iter1_4.json @@ -0,0 +1 @@ +[{"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The provided Triton operator code implements a matrix-vector multiplication kernel, `mv_kernel`. The kernel function multiplies a matrix `A` of size N x M with a vector `B` of size M, resulting in an output vector `C` of size N. The kernel is parameterized by constants `BLOCK_N` and `BLOCK_M` which determine the tile sizes for the computation, allowing for optimal parallel processing. Within `mv_kernel`, a loop iterates over the matrix A in blocks of `BLOCK_M` to load sub-matrices and the corresponding block from vector B, performing element-wise multiplication and accumulation in shared memory. The partial results are then reduced along the rows and stored in the output vector `C`. The `mv` function wraps this kernel execution for launching the kernel with calculated grid dimensions.\n ", "label": null, "file": "matrix_vector_multip.py", "test_code": "\n\n\ndef test_mv():\n # \u6d4b\u8bd5\u7528\u4f8b 2: 4x3 \u77e9\u9635\u4e0e 3x1 \u5411\u91cf\u76f8\u4e58\n A = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0], [10.0, 11.0, 12.0]], device='cuda')\n B = torch.tensor([1.0, 2.0, 3.0], device='cuda')\n triton_result_2 = mv(A, B)\n\n # \u6d4b\u8bd5\u7528\u4f8b 3: 32x16 \u77e9\u9635\u4e0e 16x1 \u5411\u91cf\u76f8\u4e58\n A = torch.randn(32, 16, device='cuda')\n B = torch.randn(16, device='cuda')\n triton_result_3 = mv(A, B)\n\n return {\n \"test_case_2\": triton_result_2,\n \"test_case_3\": triton_result_3,\n }\n\nresult_gold = test_mv()\n", "predict": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef mv_kernel(A, B, C, N, M, stride_an, stride_am, stride_bm, stride_cn, BLOCK_N: tl.constexpr, BLOCK_M: tl.constexpr):\n pid = tl.program_id(0)\n offset_n = pid * BLOCK_N + tl.arange(0, BLOCK_N)[:, None]\n offset_m = tl.arange(0, BLOCK_M)[None, :]\n n_mask = offset_n < N\n A_ptrs = A + offset_n * stride_an + offset_m * stride_am\n B_ptrs = B + offset_m * stride_bm\n acc = tl.zeros((BLOCK_N, BLOCK_M), dtype=tl.float32)\n for m in range(0, M, BLOCK_M):\n m_mask = m + offset_m < M\n a = tl.load(A_ptrs, mask=n_mask & m_mask, other=0.0).to(tl.float32)\n b = tl.load(B_ptrs, mask=m_mask, other=0.0).to(tl.float32)\n acc += a * b\n A_ptrs += BLOCK_M * stride_am\n B_ptrs += BLOCK_M * stride_bm\n acc = tl.sum(acc, axis=1)\n C_ptrs = C + offset_n * stride_cn\n tl.store(C_ptrs, acc[:, None], mask=n_mask)\n\ndef mv(inp, vec):\n assert inp.shape[1] == vec.shape[0], 'incompatible dimensions'\n N, M = inp.shape\n out = torch.empty((N,), device=inp.device, dtype=inp.dtype)\n grid = lambda META: (triton.cdiv(N, META['BLOCK_N']),)\n mv_kernel[grid](inp, vec, out, N, M, inp.stride(0), inp.stride(1), vec.stride(0), out.stride(0), BLOCK_N=64, BLOCK_M=64)\n return out\n"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The provided Triton kernel, `matmul_kernel`, is a specialized GPU matrix multiplication operation. \n It employs a blocked tiling strategy for efficient computation of the result matrix `c` from input matrices `a` and `b`. \n Within this kernel, operations are parallelized across blocks defined by BLOCK_SIZE_M, BLOCK_SIZE_N, and BLOCK_SIZE_K. \n These blocks allow the kernel to load sub-matrices, perform computations, and manage memory more efficiently.\n\n The kernel begins by computing indices for thread execution, segmenting the operation across various program IDs derived from the grid dimensions. \n For each thread block, it computes offsets `offs_am`, `offs_bn`, and `offs_k` to read data from the input matrices.\n\n In a loop iterating over slices of the K dimension, sub-matrices are loaded using `tl.load` with masks to handle boundary conditions. \n These matrices are then multiplied using `tl.dot`, accumulating results in a local accumulator. \n Memory access patterns are optimized using `tl.max_contiguous` and `tl.multiple_of` to align data in cache-friendly ways.\n\n The function finally writes the accumulated results to the output matrix `c`, with care taken to respect bounds and using conditional storage via `tl.store`.\n\n The `matmul` function wraps this kernel, preparing inputs and meta-parameters based on the matrix data types and dimensions. \n It enforces input compatibility, establishes execution grid dimensions, and sets device memory for output. \n Configuration parameters such as BLOCK_SIZE_M, num_stages, and num_warps are determined per data type, \n ensuring optimal kernel execution tailored for either float16 or Triton's experimental float8 types.\n ", "label": null, "file": "triton_matmul.py", "test_code": "\n\n\nimport torch\n\n# Test for matmul\ndef test_matmul():\n results = {}\n M, K, N = 256, 128, 256\n\n # Test case 1: torch.float16\n a = torch.randn((M, K), dtype=torch.float16, device='cuda')\n b = torch.randn((K, N), dtype=torch.float16, device='cuda')\n c = matmul(a, b)\n results['test_case_1'] = c\n\n return results\n\n# Run all tests\nresult_gold = test_matmul()", "predict": "\nimport torch\nimport triton\nimport triton.language as tl\n\ndef _matmul_launch_metadata(grid, kernel, args):\n ret = {}\n M, N, K = (args['M'], args['N'], args['K'])\n ret['name'] = f'{kernel.name} [M={M}, N={N}, K={K}]'\n if 'c_ptr' in args:\n bytes_per_elem = args['c_ptr'].element_size()\n else:\n bytes_per_elem = 2\n ret[f'flops{bytes_per_elem * 8}'] = 2.0 * M * N * K\n ret['bytes'] = bytes_per_elem * (M * K + N * K + M * N)\n return ret\n\n@triton.jit(launch_metadata=_matmul_launch_metadata)\ndef matmul_kernel(a_ptr, b_ptr, c_ptr, M, N, K, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr):\n pid = tl.program_id(axis=0)\n num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)\n num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)\n num_pid_in_group = GROUP_SIZE_M * num_pid_n\n group_id = pid // num_pid_in_group\n first_pid_m = group_id * GROUP_SIZE_M\n group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)\n pid_m = first_pid_m + pid % group_size_m\n pid_n = pid % num_pid_in_group // group_size_m\n start_m = pid_m * BLOCK_SIZE_M\n start_n = pid_n * BLOCK_SIZE_N\n offs_am = start_m + tl.arange(0, BLOCK_SIZE_M)\n offs_bn = start_n + tl.arange(0, BLOCK_SIZE_N)\n offs_am = tl.where(offs_am < M, offs_am, 0)\n offs_bn = tl.where(offs_bn < N, offs_bn, 0)\n offs_am = tl.max_contiguous(tl.multiple_of(offs_am, BLOCK_SIZE_M), BLOCK_SIZE_M)\n offs_bn = tl.max_contiguous(tl.multiple_of(offs_bn, BLOCK_SIZE_N), BLOCK_SIZE_N)\n offs_k = tl.arange(0, BLOCK_SIZE_K)\n a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)\n b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)\n accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)\n for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):\n a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)\n b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)\n accumulator = tl.dot(a, b, accumulator)\n a_ptrs += BLOCK_SIZE_K * stride_ak\n b_ptrs += BLOCK_SIZE_K * stride_bk\n c = accumulator.to(c_ptr.dtype.element_ty)\n offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)\n offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]\n c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)\n tl.store(c_ptrs, c, mask=c_mask)\n\ndef matmul(a, b):\n configs = {\n torch.float16: {'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8, 'num_stages': 1, 'num_warps': 8},\n torch.float32: {'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8, 'num_stages': 1, 'num_warps': 8}\n }\n assert a.shape[1] == b.shape[0], 'Incompatible dimensions'\n assert a.dtype == b.dtype, 'Incompatible dtypes'\n M, K = a.shape\n K, N = b.shape\n dtype = a.dtype\n c = torch.empty((M, N), device=a.device, dtype=dtype)\n grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),)\n matmul_kernel[grid](a, b, c, M, N, K, a.stride(0), a.stride(1), b.stride(0), b.stride(1), c.stride(0), c.stride(1), BLOCK_SIZE_M=configs[dtype]['BLOCK_SIZE_M'], BLOCK_SIZE_N=configs[dtype]['BLOCK_SIZE_N'], BLOCK_SIZE_K=configs[dtype]['BLOCK_SIZE_K'], GROUP_SIZE_M=configs[dtype]['GROUP_SIZE_M'], num_stages=configs[dtype]['num_stages'], num_warps=configs[dtype]['num_warps'])\n return c\n"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The Triton-accelerated function embedding_kernel is specialized for extracting and storing embedding vectors from a weight matrix for a sequence of token IDs. It uses program IDs to determine processing offsets and handles iteration over sequences with BLOCK_N and BLOCK_NN stride sizes. For each sequence, it computes token IDs and uses masks to ensure only valid data is loaded and processed. The weight matrix is addressed using a combination of token IDs and dimension offsets, facilitated by the stride of the weight tensor. The processed vectors are then stored into the 'out' tensor using calculated strides and masks, ensuring each output sequence position receives the correct embedding vector. The wrapping function, embedding, configures and invokes the kernel with appropriate grid settings, aligning BLOCK_DMODEL to the next power of two based on weight dimensions and leveraging constant memory settings to optimize the embedding extraction process.\n ", "label": null, "file": "embedding_triton_kernel.py", "test_code": "\n\n\nimport torch\n\ndef test_embedding():\n # \u53c2\u6570\u5b9a\u4e49\n vocab_size = 1000 # \u8bcd\u6c47\u8868\u5927\u5c0f\n embedding_dim = 512 # \u5d4c\u5165\u7ef4\u5ea6\n sequence_length = 128 # \u8f93\u5165\u5e8f\u5217\u957f\u5ea6\n vob_start_id = 10 # \u8bcd\u6c47\u8868\u8d77\u59cb ID\n vob_end_id = 1000 # \u8bcd\u6c47\u8868\u7ed3\u675f ID\n\n # \u521b\u5efa\u6d4b\u8bd5\u8f93\u5165\u5f20\u91cf\n input_ids = torch.randint(\n vob_start_id, vob_end_id, (sequence_length,), dtype=torch.int32, device='cuda'\n )\n weight = torch.randn(\n vocab_size, embedding_dim, dtype=torch.float32, device='cuda'\n )\n out = torch.zeros(\n sequence_length, embedding_dim, dtype=torch.float32, device='cuda'\n )\n\n # \u8c03\u7528\u5d4c\u5165\u51fd\u6570\n embedding(input_ids, weight, vob_start_id, vob_end_id, out)\n\n # \u4fdd\u5b58\u7ed3\u679c\n results = {}\n results['test_case_1'] = out.clone()\n\n # \u6d4b\u8bd5\u4e0d\u540c\u7684\u8f93\u5165\n input_ids = torch.randint(\n vob_start_id, vob_end_id, (sequence_length,), dtype=torch.int32, device='cuda'\n )\n embedding(input_ids, weight, vob_start_id, vob_end_id, out)\n results['test_case_2'] = out.clone()\n\n # \u6d4b\u8bd5\u4e0d\u540c\u7684\u8bcd\u6c47\u8868\u8303\u56f4\n vob_start_id = 0\n vob_end_id = 500\n input_ids = torch.randint(\n vob_start_id, vob_end_id, (sequence_length,), dtype=torch.int32, device='cuda'\n )\n embedding(input_ids, weight, vob_start_id, vob_end_id, out)\n results['test_case_3'] = out.clone()\n\n # \u6d4b\u8bd5\u4e0d\u540c\u7684\u5d4c\u5165\u7ef4\u5ea6\n embedding_dim = 256\n weight = torch.randn(\n vocab_size, embedding_dim, dtype=torch.float32, device='cuda'\n )\n out = torch.zeros(\n sequence_length, embedding_dim, dtype=torch.float32, device='cuda'\n )\n embedding(input_ids, weight, vob_start_id, vob_end_id, out)\n results['test_case_4'] = out.clone()\n\n return results\n\nresult_gold = test_embedding()\n", "predict": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef embedding_kernel(weight, input_ids, out, vob_start_id, vob_end_id, stride_weight_row, stride_out_row, n_ctx, hiden_size, BLOCK_N: tl.constexpr, BLOCK_DMODEL: tl.constexpr):\n pid = tl.program_id(0)\n offs_n = pid * BLOCK_N + tl.arange(0, BLOCK_N)\n offs_d = tl.arange(0, BLOCK_DMODEL)\n mask_n = offs_n < n_ctx\n token_ids_raw = tl.load(input_ids + offs_n, mask=mask_n, other=vob_end_id)\n valid_id_mask = (token_ids_raw >= vob_start_id) & (token_ids_raw < vob_end_id)\n token_ids_clamped = tl.where(valid_id_mask, token_ids_raw - vob_start_id, 0)\n offs_vec = token_ids_clamped[:, None] * stride_weight_row + offs_d[None, :]\n load_mask = valid_id_mask[:, None] & (offs_d[None, :] < hiden_size)\n vec = tl.load(weight + offs_vec, mask=load_mask, other=0.0)\n vec = tl.where(valid_id_mask[:, None], vec, 0.0)\n dest_offs = offs_n[:, None] * stride_out_row + offs_d[None, :]\n store_mask = mask_n[:, None] & (offs_d[None, :] < hiden_size)\n tl.store(out + dest_offs, vec, mask=store_mask)\n\n@torch.no_grad()\ndef embedding(input_ids: torch.Tensor, weight: torch.Tensor, vob_start_id: int, vob_end_id: int, out: torch.Tensor):\n assert input_ids.ndim == 1\n assert weight.ndim == 2\n assert out.ndim == 2 and out.shape[0] == input_ids.shape[0] and (out.shape[1] == weight.shape[1])\n n_ctx = input_ids.shape[0]\n BLOCK_DMODEL = triton.next_power_of_2(weight.shape[1])\n BLOCK_N = 128\n grid = (triton.cdiv(n_ctx, BLOCK_N),)\n embedding_kernel[grid](weight, input_ids, out, vob_start_id, vob_end_id, weight.stride(0), out.stride(0), n_ctx, weight.shape[1], BLOCK_N=BLOCK_N, BLOCK_DMODEL=BLOCK_DMODEL, num_warps=4, num_stages=1)\n\n"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The code defines a Triton-based kernel for matrix multiplication of INT4 quantized weights and provides Python functions to handle the quantization and dequantization processes. \n The 'matmul_kernel' function is a Triton kernel using @triton.jit which performs matrix multiplication. It processes the input matrices in tiles defined by BLOCK_SIZE_M, BLOCK_SIZE_N, and BLOCK_SIZE_K, and uses a loop to iterate over the K dimension to accumulate results in fp32 precision, subsequently stored in the output buffer, potentially employing atomic add for SPLIT_K > 1.\n The kernel is set up with a wide variety of configurations, allowing Triton's autotuning capabilities to select optimal parameters based on matrix dimensions M, N, and K. It utilizes quantized INT4 weights, reconstructing them using scales and zero points to compute matrix products accurately. \n The Python function 'matmul_dequantize_int4_s2' serves as an interface to this kernel, preparing data and launching the computation on specified grid dimensions.\n The function 'quantize_int4' converts a floating-point weight matrix into INT4 format, organizing the data by packing 8 INT4 values into one INT32 and calculating scaling factors and zero points for each group of elements. \n The helper function 'unpack_int4' is intended for testing; it unpacks the INT4 matrix back into a floating-point format to verify the quantization process.\n ", "label": null, "file": "int4_matmul.py", "test_code": "\n\n\ndef test_correct_int4_s2(M=32, K=4096, N=4096):\n group_size = 128\n a = torch.randn((M, K), device='cuda', dtype=torch.float16)\n b = torch.randn((K, N), device='cuda', dtype=torch.float16)\n int_b, b_scale, b_zero_point, _ = quantize_int4(b, group_size=group_size)\n \n # Test case\n triton_output = matmul_dequantize_int4_s2(a, int_b, b_scale, b_zero_point, group_size)\n \n results = {\n \"test_case_1\": triton_output\n }\n \n return results\n\nresult_gold = test_correct_int4_s2()\n", "predict": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.autotune(\n configs=[\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 1, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=8),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n triton.Config({'SPLIT_K': 2, 'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 512, 'GROUP_SIZE_M': 16}, num_stages=2, num_warps=4),\n ],\n key=['M', 'N', 'K'],\n reset_to_zero=['c_ptr']\n)\n@triton.jit\ndef matmul_kernel(a_ptr, b_ptr, c_ptr, bs_ptr, bzp_ptr, M, N, K, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, stride_bsk, stride_bsn, stride_bzpk, stride_bzpn, group_size, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr, SPLIT_K: tl.constexpr):\n pid = tl.program_id(axis=0)\n pid_sp_k = tl.program_id(axis=1)\n num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)\n num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)\n num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)\n num_pid_in_group = GROUP_SIZE_M * num_pid_n\n group_id = pid // num_pid_in_group\n first_pid_m = group_id * GROUP_SIZE_M\n group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)\n pid_m = first_pid_m + pid % group_size_m\n pid_n = pid % num_pid_in_group // group_size_m\n offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M\n offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N\n offs_k = pid_sp_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)\n a_ptrs = a_ptr + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak\n b_ptrs = b_ptr + offs_k[:, None] // 8 * stride_bk + offs_bn[None, :] * stride_bn\n accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)\n for k in range(0, tl.cdiv(K, BLOCK_SIZE_K * SPLIT_K)):\n bs_ptrs = bs_ptr + (offs_k[:, None] + k * BLOCK_SIZE_K * SPLIT_K) // group_size * stride_bsk + offs_bn[None, :] * stride_bsn\n bzp_ptrs = bzp_ptr + (offs_k[:, None] + k * BLOCK_SIZE_K * SPLIT_K) // group_size * stride_bzpk + offs_bn[None, :] // 8 * stride_bzpn\n b_shift_bits = (offs_k[:, None] % 8) * 4\n bzp_shift_bits = (offs_bn[None, :] % 8) * 4\n a = tl.load(a_ptrs)\n b = tl.load(b_ptrs)\n bs = tl.load(bs_ptrs)\n bzp = tl.load(bzp_ptrs)\n int_b = (b >> b_shift_bits) & 15\n int_bzp = (bzp >> bzp_shift_bits) & 15\n b = ((int_b - int_bzp) * bs).to(a.dtype)\n accumulator += tl.dot(a, b)\n a_ptrs += BLOCK_SIZE_K * SPLIT_K * stride_ak\n b_ptrs += BLOCK_SIZE_K * SPLIT_K * stride_bk // 8\n c = accumulator.to(c_ptr.dtype.element_ty)\n offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)\n offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]\n c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)\n if SPLIT_K == 1:\n tl.store(c_ptrs, c, mask=c_mask)\n else:\n tl.atomic_add(c_ptrs, c, mask=c_mask)\n\ndef matmul_dequantize_int4_s2(x: torch.FloatTensor, qweight: torch.IntTensor, scales: torch.FloatTensor, qzeros: torch.IntTensor, group_size: int=128, output=None) -> torch.FloatTensor:\n assert x.is_contiguous(), 'A must be contiguous'\n assert qweight.is_contiguous(), 'B must be contiguous'\n M, K = x.shape\n N = scales.shape[1]\n if output is None:\n output = torch.zeros((M, N), device=x.device, dtype=x.dtype)\n grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), META['SPLIT_K'])\n matmul_kernel[grid](\n x, qweight, output, scales, qzeros,\n M, N, K,\n x.stride(0), x.stride(1),\n qweight.stride(0), qweight.stride(1),\n output.stride(0), output.stride(1),\n scales.stride(0), scales.stride(1),\n qzeros.stride(0), qzeros.stride(1),\n group_size\n )\n return output\n\ndef quantize_int4(weight, group_size=128, tp_rank=0):\n weight = weight.transpose(1, 0)\n h1, h2 = weight.shape\n assert h1 % 8 == 0 and h2 % 8 == 0, 'H1 {} H2 {}'.format(h1, h2)\n assert h2 % group_size == 0, 'H1 {} H2 {}'.format(h1, h2)\n weight = weight.contiguous().view(-1, group_size).cuda(tp_rank)\n weight_max = weight.amax(-1, keepdim=True)\n weight_max = torch.where(weight_max < 0, 0, weight_max)\n weight_min = weight.amin(-1, keepdim=True)\n weight_min = torch.where(weight_min > 0, 0, weight_min)\n weight_range = weight_max - weight_min\n scale = weight_range / (2 ** 4 - 1)\n zero_point = (-weight_min / scale).round().clamp(0, 15).to(torch.int32)\n weight = (weight / scale + zero_point).round().clamp(0, 15).to(torch.int32).view(h1, h2)\n int_weight = torch.empty(h1, h2 // 8, dtype=torch.int32, device=weight.device)\n int_zero_point = torch.zeros(h1 // 8, h2 // group_size, dtype=torch.int32, device=weight.device)\n zero_point = zero_point.view(h1, -1)\n scale = scale.view(h1, -1)\n for pack in range(0, h2, 8):\n for i in range(8):\n int_weight[:, pack // 8] += weight[:, pack + i] << (i * 4)\n for pack in range(0, h1, 8):\n for i in range(8):\n int_zero_point[pack // 8, :] += zero_point[pack + i, :] << (i * 4)\n return (int_weight.transpose(1, 0).contiguous(), scale.transpose(1, 0).contiguous(), int_zero_point.transpose(1, 0).contiguous(), group_size)\n\ndef unpack_int4(weight, scale, zp):\n weight = weight.transpose(1, 0)\n scale = scale.transpose(1, 0)\n zp = zp.transpose(1, 0)\n h1, h2 = weight.shape\n group_size = h2 * 8 // scale.shape[1]\n group_num = scale.shape[1]\n fp_weight = torch.zeros(h1, h2 * 8, dtype=torch.half, device=weight.device)\n fp_zero_point = torch.zeros(h1, group_num, device=weight.device)\n for pack in range(0, h2):\n for i in range(8):\n fp_weight[:, pack * 8 + i] = (weight[:, pack] >> (i * 4)) & 15\n for pack in range(0, h1 // 8):\n for i in range(8):\n fp_zero_point[pack * 8 + i, :] = (zp[pack, :] >> (i * 4)) & 15\n for g in range(group_num):\n fp_weight[:, g * group_size:(g + 1) * group_size] = (fp_weight[:, g * group_size:(g + 1) * group_size] - fp_zero_point[:, g].unsqueeze(1)) * scale[:, g].unsqueeze(1)\n return fp_weight.transpose(1, 0)\n"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The `_fwd_kernel_flash_decode_stage2` Triton kernel is a parallel computation designed for processing sequences in a neural network context, specifically dealing with batches, heads, and sequence blocks. This kernel receives several inputs: `B_Seqlen`, `Mid_O`, `Mid_O_LogExpSum`, and `Out`, along with strides for indexing. `B_Seqlen` contains sequence lengths per batch, `Mid_O` contains intermediate outputs, `Mid_O_LogExpSum` holds log-exp sum values, and `Out` will store the final output. The kernel operates over a 2D grid defined by batch size and head count (`grid = (batch, head_num)`), with constants `BLOCK_SEQ` and `BLOCK_DMODEL` indicating sequence block size and dimension alignment respectively.\n\n The kernel function operates as follows:\n - Identifies the current batch and head using `tl.program_id`.\n - Initializes accumulators: `sum_exp`, `max_logic`, and `acc` to accumulate exponential logic and values.\n - Loads the current sequence length and calculates the number of sequence blocks (`block_n_size`).\n - Iterates over each block, where:\n - It loads values (`tv`) from `Mid_O` and logic sums (`tlogic`) from `Mid_O_LogExpSum`.\n - Computes the maximum logic value across blocks and scales previous accumulations.\n - Updates the accumulators by computing the exponential of adjusted logic values and scaling/accumulating.\n - Stores the final normalized result into `Out`, scaling accumulated values by the sum of exponentials.\n\n The `flash_decode_stage2` function sets up and invokes this kernel, determining dimensions and grid setup based on input tensor shapes. It ensures efficient computation by using Triton's parallel execution framework, specifying warp and stage numbers.\n ", "label": null, "file": "flash_decode2_phi.py", "test_code": "\n\n\nimport torch\n\n# Define the test function\ndef test_flash_decode_stage2():\n # Define the parameters for different test cases\n batch_size = 2\n head_num = 4\n seq_block_num = 3\n head_dim = 64\n block_seq = 16\n\n test_cases = {\n \"test_case_1\": {\n \"B_Seqlen\": torch.randint(1, seq_block_num * block_seq, (batch_size,), dtype=torch.int32, device='cuda'),\n \"mid_out\": torch.randn((batch_size, head_num, seq_block_num, head_dim), dtype=torch.float32, device='cuda'),\n \"mid_out_logexpsum\": torch.randn((batch_size, head_num, seq_block_num), dtype=torch.float32, device='cuda'),\n \"Out\": torch.zeros((batch_size, head_num, head_dim), dtype=torch.float32, device='cuda'),\n \"block_seq\": block_seq\n },\n \"test_case_2\": {\n \"B_Seqlen\": torch.randint(1, seq_block_num * block_seq, (batch_size,), dtype=torch.int32, device='cuda'),\n \"mid_out\": torch.randn((batch_size, head_num, seq_block_num, head_dim), dtype=torch.float32, device='cuda'),\n \"mid_out_logexpsum\": torch.randn((batch_size, head_num, seq_block_num), dtype=torch.float32, device='cuda'),\n \"Out\": torch.zeros((batch_size, head_num, head_dim), dtype=torch.float32, device='cuda'),\n \"block_seq\": block_seq + 1 # Different block size\n },\n \"test_case_3\": {\n \"B_Seqlen\": torch.randint(1, seq_block_num * block_seq, (batch_size,), dtype=torch.int32, device='cuda'),\n \"mid_out\": torch.randn((batch_size, head_num, seq_block_num, head_dim), dtype=torch.float32, device='cuda'),\n \"mid_out_logexpsum\": torch.randn((batch_size, head_num, seq_block_num), dtype=torch.float32, device='cuda'),\n \"Out\": torch.zeros((batch_size, head_num, head_dim), dtype=torch.float32, device='cuda'),\n \"block_seq\": block_seq // 2 # Different block size\n },\n \"test_case_4\": {\n \"B_Seqlen\": torch.randint(1, seq_block_num * block_seq, (batch_size,), dtype=torch.int32, device='cuda'),\n \"mid_out\": torch.randn((batch_size, head_num, seq_block_num, head_dim), dtype=torch.float32, device='cuda'),\n \"mid_out_logexpsum\": torch.randn((batch_size, head_num, seq_block_num), dtype=torch.float32, device='cuda'),\n \"Out\": torch.zeros((batch_size, head_num, head_dim), dtype=torch.float32, device='cuda'),\n \"block_seq\": block_seq * 2 # Different block size\n }\n }\n\n # Execute the function for all test cases\n results = {}\n for key, test_case in test_cases.items():\n flash_decode_stage2(test_case[\"mid_out\"], test_case[\"mid_out_logexpsum\"], test_case[\"B_Seqlen\"], test_case[\"Out\"], test_case[\"block_seq\"])\n results[key] = test_case[\"Out\"]\n\n return results\n\n# Run the test\nresult_gold = test_flash_decode_stage2()\n", "predict": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef _fwd_kernel_flash_decode_stage2(B_Seqlen, Mid_O, Mid_O_LogExpSum, Out,\n stride_mid_ob, stride_mid_oh, stride_mid_os, stride_mid_od,\n stride_mid_o_eb, stride_mid_o_eh, stride_mid_o_es,\n stride_obs, stride_oh, stride_od,\n head_dim,\n BLOCK_SEQ: tl.constexpr, BLOCK_DMODEL: tl.constexpr):\n cur_batch = tl.program_id(0)\n cur_head = tl.program_id(1)\n\n offs_d = tl.arange(0, BLOCK_DMODEL)\n\n cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)\n block_n_size = (cur_batch_seq_len + BLOCK_SEQ - 1) // BLOCK_SEQ\n\n sum_exp = 0.0\n max_logic = -float('inf')\n acc = tl.zeros([BLOCK_DMODEL], dtype=tl.float32)\n\n for block_seq_n in range(0, block_n_size):\n tv = tl.load(Mid_O +\n cur_batch * stride_mid_ob +\n cur_head * stride_mid_oh +\n block_seq_n * stride_mid_os +\n offs_d,\n mask=offs_d < head_dim, other=0.0)\n\n tlogic = tl.load(Mid_O_LogExpSum +\n cur_batch * stride_mid_o_eb +\n cur_head * stride_mid_o_eh +\n block_seq_n)\n\n new_max_logic = tl.maximum(tlogic, max_logic)\n old_scale = tl.exp(max_logic - new_max_logic)\n acc *= old_scale\n exp_logic = tl.exp(tlogic - new_max_logic)\n acc += exp_logic * tv\n sum_exp = sum_exp * old_scale + exp_logic\n max_logic = new_max_logic\n\n tl.store(Out +\n cur_batch * stride_obs +\n cur_head * stride_oh +\n offs_d,\n acc / sum_exp,\n mask=offs_d < head_dim)\n\n@torch.no_grad()\ndef flash_decode_stage2(mid_out, mid_out_logexpsum, B_Seqlen, Out, block_seq):\n Lk = mid_out.shape[-1]\n head_dim = Lk\n batch, head_num = mid_out.shape[0], mid_out.shape[1]\n BLOCK_DMODEL = triton.next_power_of_2(head_dim)\n grid = (batch, head_num)\n _fwd_kernel_flash_decode_stage2[grid](\n B_Seqlen,\n mid_out, mid_out_logexpsum, Out,\n mid_out.stride(0), mid_out.stride(1), mid_out.stride(2), mid_out.stride(3),\n mid_out_logexpsum.stride(0), mid_out_logexpsum.stride(1), mid_out_logexpsum.stride(2),\n Out.stride(0), Out.stride(1), Out.stride(2),\n head_dim,\n BLOCK_SEQ=block_seq, BLOCK_DMODEL=BLOCK_DMODEL,\n num_warps=4, num_stages=2\n )\n"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\nThe Triton operator is defined to transpose a matrix using a kernel function and a wrapper function. The kernel function named 'kernel' is decorated with '@triton.jit' for just-in-time compilation and performs matrix transposition by directly manipulating pointers based on the given strides and dimensions. It accepts input parameters such as a matrix 'M', an output buffer 'Out', the strides of 'M' and 'Out', and the dimensions 'SIZE_M' and 'D_HEAD'. The kernel computes the pointers for elements of 'M' using 'matrix_stridex' and 'matrix_stridey', and for 'Out' using 'out_stridex' and 'out_stridey'. The transposition is achieved by loading elements from 'M' and storing them into 'Out' in a transposed layout. The wrapper function named 'wrapper' initializes 'matrix' with random float16 values and 'out' with zeros, both on CUDA. It defines the grid configuration as a tuple with a single element, then calls the kernel with these matrices and their properties. Finally, it returns the transposed matrix 'out'.\n ", "label": null, "file": "matrix_transpose.py", "test_code": "\n\n\nimport torch\n\ndef test_triton_vs_torch():\n results = {}\n\n # \u6d4b\u8bd5\u7528\u4f8b 1: \u57fa\u672c\u77e9\u9635\u8f6c\u7f6e (\u5c0f\u77e9\u9635)\n size_m, d_head = 16, 16\n out = wrapper(size_m, d_head)\n results[\"test_case_1\"] = out.clone()\n\n # \u6d4b\u8bd5\u7528\u4f8b 2: \u975e\u65b9\u5f62\u77e9\u9635\n size_m, d_head = 32, 64\n out = wrapper(size_m, d_head)\n results[\"test_case_2\"] = out.clone()\n\n return results\n\n\n# \u8fd0\u884c\u6d4b\u8bd5\nresult_gold = test_triton_vs_torch()\n# print(result_gold)", "predict": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef kernel(M, Out, matrix_stridex, matrix_stridey, out_stridex, out_stridey, SIZE_M, D_HEAD, BLOCK_M: tl.constexpr, BLOCK_D: tl.constexpr):\n pid_m = tl.program_id(0)\n pid_d = tl.program_id(1)\n offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)\n offs_d = pid_d * BLOCK_D + tl.arange(0, BLOCK_D)\n mask_m = offs_m < SIZE_M\n mask_d = offs_d < D_HEAD\n inp_ptrs = M + offs_m[:, None] * matrix_stridex + offs_d[None, :] * matrix_stridey\n out_ptrs = Out + offs_d[:, None] * out_stridex + offs_m[None, :] * out_stridey\n tile = tl.load(inp_ptrs, mask=mask_m[:, None] & mask_d[None, :], other=0.0)\n tl.store(out_ptrs, tile.T, mask=mask_d[:, None] & mask_m[None, :])\n\ndef wrapper(size_m: int, d_head: int) -> torch.Tensor:\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n matrix = torch.randn((size_m, d_head), dtype=torch.float16, device=device)\n out = torch.empty((d_head, size_m), dtype=torch.float16, device=device)\n BLOCK_M = 16\n BLOCK_D = 16\n grid = (triton.cdiv(size_m, BLOCK_M), triton.cdiv(d_head, BLOCK_D))\n kernel[grid](matrix, out, matrix.stride(0), matrix.stride(1), out.stride(0), out.stride(1), size_m, d_head, BLOCK_M=BLOCK_M, BLOCK_D=BLOCK_D)\n return out\n\n"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The `rotary_kernel` function is a Triton kernel that performs rotary position encoding on a tensor `X` using precomputed cosine (`COS`) and sine (`SIN`) matrices. It modifies or populates the output tensor `OUT` with the transformed data. The kernel accommodates both fixed and variable sequence lengths, controlled by the presence of `CU_SEQLENS`. The kernel handles interleaved and non-interleaved formats and allows for in-place transformations and conjugate computations if specified.\n\n The kernel operates in a three-dimensional grid, processing batches (`pid_batch`), heads (`pid_head`), and sequences (`pid_m`). It calculates transformations by loading blocks of data and applying rotary transformations based on cosine and sine values. The key operations are tailored based on whether the data is interleaved or not, with conditional handling for conjugation using `CONJUGATE`.\n\n The `apply_rotary` function acts as a high-level interface to the Triton kernel. It accepts the input tensor `x`, cosine and sine matrices, sequence length offsets, and optional cumulative sequence lengths (`cu_seqlens`). The function determines the execution grid and block sizes, aligning them with the input data shape and configuration. It initializes an output tensor, copying non-rotary parts of `x` if required. The function ensures that the kernel is called with appropriate arguments, matching the shape and type expectations set within the kernel logic. This design allows for efficient rotary transformations in transformer architectures.\n ", "label": null, "file": "rotary_transform.py", "test_code": "\n\n\nimport torch\n\ndef test_apply_rotary():\n results = {}\n \n # Test case 1: Basic test with fixed sequence length and no interleaving\n batch, seqlen, nheads, headdim = 2, 128, 4, 64\n rotary_dim = 32\n x = torch.randn(batch, seqlen, nheads, headdim, device='cuda')\n cos = torch.randn(seqlen, rotary_dim // 2, device='cuda')\n sin = torch.randn(seqlen, rotary_dim // 2, device='cuda')\n output = apply_rotary(x, cos, sin)\n results['test_case_1'] = output.shape\n\n # Test case 2: Variable length sequences with interleaving\n total_seqlen, nheads, headdim = 256, 4, 64\n batch = 3\n cu_seqlens = torch.tensor([0, 100, 200, 256], device='cuda')\n max_seqlen = 128\n rotary_dim = 32\n x = torch.randn(total_seqlen, nheads, headdim, device='cuda')\n cos = torch.randn(max_seqlen, rotary_dim // 2, device='cuda')\n sin = torch.randn(max_seqlen, rotary_dim // 2, device='cuda')\n output = apply_rotary(x, cos, sin, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen, interleaved=True)\n results['test_case_2'] = output.shape\n\n # Test case 3: Conjugate flag enabled\n batch, seqlen, nheads, headdim = 2, 128, 4, 64\n rotary_dim = 32\n x = torch.randn(batch, seqlen, nheads, headdim, device='cuda')\n cos = torch.randn(seqlen, rotary_dim // 2, device='cuda')\n sin = torch.randn(seqlen, rotary_dim // 2, device='cuda')\n output = apply_rotary(x, cos, sin, conjugate=True)\n results['test_case_3'] = output.shape\n\n # Test case 4: Inplace operation\n batch, seqlen, nheads, headdim = 2, 128, 4, 64\n rotary_dim = 32\n x = torch.randn(batch, seqlen, nheads, headdim, device='cuda')\n cos = torch.randn(seqlen, rotary_dim // 2, device='cuda')\n sin = torch.randn(seqlen, rotary_dim // 2, device='cuda')\n output = apply_rotary(x, cos, sin, inplace=True)\n results['test_case_4'] = output.shape\n\n return results\n\nresult_gold = test_apply_rotary()\n", "predict": "\nfrom typing import Optional, Union\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef rotary_kernel(OUT, X, COS, SIN, CU_SEQLENS, SEQLEN_OFFSETS, seqlen, nheads, rotary_dim, seqlen_ro, CACHE_KEY_SEQLEN, stride_out_batch, stride_out_seqlen, stride_out_nheads, stride_out_headdim, stride_x_batch, stride_x_seqlen, stride_x_nheads, stride_x_headdim, BLOCK_K: tl.constexpr, IS_SEQLEN_OFFSETS_TENSOR: tl.constexpr, IS_VARLEN: tl.constexpr, INTERLEAVED: tl.constexpr, CONJUGATE: tl.constexpr, BLOCK_M: tl.constexpr):\n pid_m = tl.program_id(axis=0)\n pid_batch = tl.program_id(axis=1)\n pid_head = tl.program_id(axis=2)\n rotary_dim_half = rotary_dim // 2\n if not IS_VARLEN:\n X = X + pid_batch * stride_x_batch + pid_head * stride_x_nheads\n OUT = OUT + pid_batch * stride_out_batch + pid_head * stride_out_nheads\n else:\n start_idx = tl.load(CU_SEQLENS + pid_batch)\n seqlen = tl.load(CU_SEQLENS + pid_batch + 1) - start_idx\n X = X + start_idx * stride_x_seqlen + pid_head * stride_x_nheads\n OUT = OUT + start_idx * stride_out_seqlen + pid_head * stride_out_nheads\n if pid_m * BLOCK_M >= seqlen:\n return\n rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)\n if not IS_SEQLEN_OFFSETS_TENSOR:\n rm_cs = rm + SEQLEN_OFFSETS\n else:\n rm_cs = rm + tl.load(SEQLEN_OFFSETS + pid_batch)\n rk = tl.arange(0, BLOCK_K)\n rk_half = tl.arange(0, BLOCK_K // 2)\n if not INTERLEAVED:\n X = X + (rm[:, None] * stride_x_seqlen + rk_half[None, :] * stride_x_headdim)\n COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :])\n SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :])\n cos = tl.load(COS, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=1.0).to(tl.float32)\n sin = tl.load(SIN, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=0.0).to(tl.float32)\n x0 = tl.load(X, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), other=0.0).to(tl.float32)\n x1 = tl.load(X + rotary_dim_half * stride_x_headdim, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), other=0.0).to(tl.float32)\n if CONJUGATE:\n sin = -sin\n o0 = x0 * cos - x1 * sin\n o1 = x0 * sin + x1 * cos\n OUT = OUT + (rm[:, None] * stride_out_seqlen + rk_half[None, :] * stride_out_headdim)\n tl.store(OUT, o0, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half))\n tl.store(OUT + rotary_dim_half * stride_out_headdim, o1, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half))\n else:\n rk_swap = rk + (rk + 1) % 2 * 2 - 1\n rk_repeat = tl.arange(0, BLOCK_K) // 2\n X0 = X + (rm[:, None] * stride_x_seqlen + rk[None, :] * stride_x_headdim)\n X1 = X + (rm[:, None] * stride_x_seqlen + rk_swap[None, :] * stride_x_headdim)\n COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :])\n SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :])\n cos = tl.load(COS, mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half), other=1.0).to(tl.float32)\n sin = tl.load(SIN, mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half), other=0.0).to(tl.float32)\n x0 = tl.load(X0, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim), other=0.0).to(tl.float32)\n x1 = tl.load(X1, mask=(rm[:, None] < seqlen) & (rk_swap[None, :] < rotary_dim), other=0.0).to(tl.float32)\n if CONJUGATE:\n sin = -sin\n x0_cos = x0 * cos\n x1_sin = x1 * sin\n out = tl.where(rk[None, :] % 2 == 0, x0_cos - x1_sin, x0_cos + x1_sin)\n OUT = OUT + (rm[:, None] * stride_out_seqlen + rk[None, :] * stride_out_headdim)\n tl.store(OUT, out, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim))\n\ndef apply_rotary(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, seqlen_offsets: Union[int, torch.Tensor]=0, cu_seqlens: Optional[torch.Tensor]=None, max_seqlen: Optional[int]=None, interleaved=False, inplace=False, conjugate=False) -> torch.Tensor:\n is_varlen = cu_seqlens is not None\n if not is_varlen:\n batch, seqlen, nheads, headdim = x.shape\n else:\n total_seqlen, nheads, headdim = x.shape\n batch = cu_seqlens.shape[0] - 1\n seqlen = max_seqlen\n seqlen_ro, rotary_dim = cos.shape\n rotary_dim *= 2\n cos, sin = (cos.contiguous(), sin.contiguous())\n if isinstance(seqlen_offsets, torch.Tensor):\n seqlen_offsets = seqlen_offsets.contiguous()\n else:\n seqlen_offsets += seqlen\n output = torch.empty_like(x) if not inplace else x\n if rotary_dim < headdim and (not inplace):\n output[..., rotary_dim:].copy_(x[..., rotary_dim:])\n BLOCK_K = 32 if rotary_dim <= 32 else 64 if rotary_dim <= 64 else 128 if rotary_dim <= 128 else 256\n grid = lambda META: (triton.cdiv(seqlen, META['BLOCK_M']), batch, nheads)\n BLOCK_M = 4 if interleaved else 8 if rotary_dim <= 64 else 4\n with torch.cuda.device(x.device.index):\n rotary_kernel[grid](output, x, cos, sin, cu_seqlens, seqlen_offsets, seqlen, nheads, rotary_dim, seqlen_ro, seqlen // 128, output.stride(0) if not is_varlen else 0, output.stride(-3), output.stride(-2), output.stride(-1), x.stride(0) if not is_varlen else 0, x.stride(-3), x.stride(-2), x.stride(-1), BLOCK_K, isinstance(seqlen_offsets, torch.Tensor), is_varlen, interleaved, conjugate, BLOCK_M)\n return output\n"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The code implements a Triton kernel named `kernel_function`, which processes input data using block-wise operations. \n The kernel takes pointers to input and output data (`x_ptr` and `output_ptr`), the total number of elements to process (`n_elements`), and a constant block size (`BLOCK_SIZE`). \n Inside the kernel, each program instance calculates its starting point (`block_start`) and creates an `offsets` tensor for element indexing. \n A mask ensures operations only occur on valid indices within the input bounds. The kernel loads data from `x_ptr`, computes the sine using `tl.math.sin`, and stores the result in `output_ptr`. \n The `call_kernel` function prepares to execute the kernel by calculating the total number of elements (`n_elements`) and creates an output tensor. \n It defines a grid configuration function using lambda to handle thread block calculations based on `BLOCK_SIZE`, ensuring the entire input is processed. \n The kernel is then launched with the grid configuration, input, output, and element count.\n ", "label": null, "file": "sin_kernel.py", "test_code": "\n\n\nimport torch\n\n# Function to test the Triton kernel\ndef test_call_kernel():\n results = {}\n \n # Test case 1: Small input tensor\n x1 = torch.tensor([0.0, 1.0, 2.0, 3.0], dtype=torch.float32).cuda()\n output1 = call_kernel(x1)\n results['test_case_1'] = output1\n \n # Test case 2: Larger input tensor\n x2 = torch.linspace(0, 10, steps=1024, dtype=torch.float32).cuda()\n output2 = call_kernel(x2)\n results['test_case_2'] = output2\n\n # Test case 3: Edge case with zero elements\n x3 = torch.tensor([], dtype=torch.float32).cuda()\n output3 = call_kernel(x3)\n results['test_case_3'] = output3\n\n # Test case 4: Input tensor with negative values\n x4 = torch.tensor([-1.0, -2.0, -3.0, -4.0], dtype=torch.float32).cuda()\n output4 = call_kernel(x4)\n results['test_case_4'] = output4\n \n return results\n\n# Run the test function\nresult_gold = test_call_kernel()\n", "predict": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef kernel_function(x_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):\n pid = tl.program_id(0)\n block_start = pid * BLOCK_SIZE\n offsets = block_start + tl.arange(0, BLOCK_SIZE)\n mask = offsets < n_elements\n x = tl.load(x_ptr + offsets, mask=mask)\n output = tl.math.sin(x)\n tl.store(output_ptr + offsets, output, mask=mask)\n\ndef call_kernel(x):\n n_elements = x.numel()\n output = torch.empty_like(x)\n grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n kernel_function[grid](x, output, n_elements, BLOCK_SIZE=1024)\n return output\n"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The Triton kernel `_l2_norm_bwd_kernel` performs a backward pass operation for L2 normalization on a per-row basis. It receives pointers to input `X`, output gradient `DY`, and calculates the input gradient `DX`. Each row of the input is accessed using the `stride_x_row`. `BLOCK_N` determines the number of elements processed per block, set based on maximum allowable fused size and next power of 2 of `N`. Within the kernel, it computes the variance of the input slice, uses it to compute the reciprocal of the standard deviation (`rstd`), and then calculates `dx` using the formula `dy * rstd - tl.sum(dy * x) * (1 / (var+eps)) * rstd * x`. The result is conditionally stored in `DX` using masks. The `_l2_norm_bwd` function orchestrates this process, ensuring input tensors `x` and `dy` are properly reshaped and their strides configured for contiguity if necessary. If `N` exceeds `BLOCK_N`, an error is raised to prevent excessive feature dimensions. Finally, the kernel is launched over `M` rows of the reshaped tensors, and the output `dx` is reshaped back to the original input shape.\n ", "label": null, "file": "l2_norm_bwd.py", "test_code": "\n\n\nimport torch\n\n# Test the backward L2 normalization\ndef test_l2_norm_bwd():\n results = {}\n \n # Test case 1: Default case\n x = torch.randn(4, 8, device='cuda', dtype=torch.float32)\n dy = torch.randn(4, 8, device='cuda', dtype=torch.float32)\n dx = _l2_norm_bwd(x, dy)\n results['test_case_1'] = dx\n\n # Test case 2: Different shape\n x = torch.randn(2, 16, device='cuda', dtype=torch.float32)\n dy = torch.randn(2, 16, device='cuda', dtype=torch.float32)\n dx = _l2_norm_bwd(x, dy)\n results['test_case_2'] = dx\n\n # Test case 3: Larger tensor\n x = torch.randn(8, 8, device='cuda', dtype=torch.float32)\n dy = torch.randn(8, 8, device='cuda', dtype=torch.float32)\n dx = _l2_norm_bwd(x, dy)\n results['test_case_3'] = dx\n\n # Test case 4: Edge case with small tensor\n x = torch.randn(1, 8, device='cuda', dtype=torch.float32)\n dy = torch.randn(1, 8, device='cuda', dtype=torch.float32)\n dx = _l2_norm_bwd(x, dy)\n results['test_case_4'] = dx\n\n return results\n\n# Run the tests\nresult_gold = test_l2_norm_bwd()\n", "predict": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef _l2_norm_bwd_kernel(X, DY, DX, stride_x_row, N, eps, BLOCK_N: tl.constexpr):\n row = tl.program_id(0)\n X += row * stride_x_row\n DX += row * stride_x_row\n DY += row * stride_x_row\n cols = tl.arange(0, BLOCK_N)\n mask = cols < N\n x = tl.load(X + cols, mask=mask, other=0.0).to(tl.float32)\n dy = tl.load(DY + cols, mask=mask, other=0.0).to(tl.float32)\n var = tl.sum(x * x)\n rstd = 1 / tl.math.sqrt(var + eps)\n dx = dy * rstd - tl.sum(dy * x) * (1 / (var + eps)) * rstd * x\n tl.store(DX + cols, dx, mask=mask)\n\ndef _l2_norm_bwd(x: torch.Tensor, dy: torch.Tensor, eps: float = 1e-05):\n x_shape_og = x.shape\n x = x.reshape(-1, dy.shape[-1])\n dy = dy.reshape(-1, dy.shape[-1])\n if dy.stride(-1) != 1:\n dy = dy.contiguous()\n dx = torch.empty_like(x)\n N = x.shape[-1]\n M = x.shape[0]\n MAX_FUSED_SIZE = 65536 // x.element_size()\n BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))\n if N > BLOCK_N:\n raise RuntimeError(\"This layer norm doesn't support feature dim >= 64KB.\")\n _l2_norm_bwd_kernel[(M,)](\n x, dy, dx,\n x.stride(0),\n N,\n eps,\n BLOCK_N=BLOCK_N\n )\n return dx.reshape(x_shape_og)\n"}, {"instruction": "You are a expert in writing Triton operators for efficient GPU programming. Use triton language write a kernel and wrapper according following instruction.\n The Triton kernel `_l2_norm_fwd_1pass_kernel` aims to perform L2 normalization on a 2D input tensor `X`. It processes each row separately using Triton's parallel execution model. The kernel expects pointers to `X` and `Y` along with the stride for rows (`stride_x_row`), number of columns in `X` (`N`), a small constant `eps` to prevent division by zero, and a compile-time constant `BLOCK_N`. The kernel computes L2 normalization by first loading a block of data from `X`, calculating the sum of squares for variance, and computing the reciprocal of the square root of the variance plus `eps` to get `rstd`. It then multiplies the input block by `rstd` to produce the normalized values, which are stored in `Y`.\n\n The Python function `_l2_norm_fwd` handles the setup and execution of the kernel. It first reshapes and possibly makes the input tensor `x` contiguous. It initializes an empty tensor `y` to store the output. The function calculates `BLOCK_N` based on `x`'s element size and ensures it doesn't exceed 64KB. If the feature dimension `N` is larger than `BLOCK_N`, it raises an error. The kernel is then launched with the total number of rows `M`, pointers to `x` and `y`, stride, number of columns, `eps`, and `BLOCK_N`. Finally, the function returns the normalized tensor reshaped to its original dimensions.\n ", "label": null, "file": "l2_norm_triton1.py", "test_code": "\n\n\nimport torch\n\n# Test the forward L2 normalization\ndef test_l2_norm_fwd():\n results = {}\n \n # Test case 1\n x1 = torch.randn(4, 8, device='cuda', dtype=torch.float32)\n y1 = _l2_norm_fwd(x1)\n results['test_case_1'] = y1\n\n # Test case 2: Different batch size\n x2 = torch.randn(2, 8, device='cuda', dtype=torch.float32)\n y2 = _l2_norm_fwd(x2)\n results['test_case_2'] = y2\n\n # Test case 3: Different feature size\n x3 = torch.randn(4, 4, device='cuda', dtype=torch.float32)\n y3 = _l2_norm_fwd(x3)\n results['test_case_3'] = y3\n\n # Test case 4: Larger tensor\n x4 = torch.randn(8, 8, device='cuda', dtype=torch.float32)\n y4 = _l2_norm_fwd(x4)\n results['test_case_4'] = y4\n\n return results\n\nresult_gold = test_l2_norm_fwd()\n", "predict": "\nimport torch\nimport triton\nimport triton.language as tl\n\n@triton.jit\ndef _l2_norm_fwd_1pass_kernel(X, Y, stride_x_row, N, eps, BLOCK_N: tl.constexpr):\n row = tl.program_id(0)\n X += row * stride_x_row\n Y += row * stride_x_row\n cols = tl.arange(0, BLOCK_N)\n mask = cols < N\n x = tl.load(X + cols, mask=mask, other=0.0).to(tl.float32)\n var = tl.sum(x * x, axis=0)\n rstd = 1 / tl.sqrt(var + eps)\n y = x * rstd\n tl.store(Y + cols, y, mask=mask)\n\ndef _l2_norm_fwd(x, eps=1e-06):\n x_shape_og = x.shape\n x = x.reshape(-1, x.shape[-1])\n if x.stride(-1) != 1:\n x = x.contiguous()\n y = torch.empty_like(x)\n N = x.shape[-1]\n M = x.shape[0]\n MAX_FUSED_SIZE = 65536 // x.element_size()\n BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))\n if N > BLOCK_N:\n raise RuntimeError(\"This layer norm doesn't support feature dim >= 64KB.\")\n grid = (M,)\n _l2_norm_fwd_1pass_kernel[grid](\n x, y,\n stride_x_row=x.stride(0),\n N=N,\n eps=eps,\n BLOCK_N=BLOCK_N\n )\n return y.reshape(x_shape_og)\n"}] \ No newline at end of file