migrate da strategy provider to alkaid #1518
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -115,8 +115,9 @@ def match(self, node): | |
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| @requires('da') | ||
| def transform(self, model: 'ModelGraph', node: Layer): | ||
| from alkaid.cmvm import solver_options_t | ||
| from alkaid.codegen.hls import hls_logic_and_bridge_gen | ||
| from alkaid.trace import FVArray, trace | ||
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| kernel: np.ndarray = node.attributes['weight'].data | ||
| kernel = kernel.reshape(-1, kernel.shape[-1]) | ||
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@@ -125,23 +126,22 @@ def transform(self, model: 'ModelGraph', node: Layer): | |
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| k, i, f = get_kernel_inp_kif(node) | ||
| hard_dc = int(os.environ.get('DA_HARD_DC', 2)) | ||
| options: solver_options_t = {'hard_dc': hard_dc, 'search_all_decompose_dc': True} | ||
| inp = FVArray.from_kif(k, i, f, solver_options=options) | ||
| out = inp @ kernel | ||
| if node.attributes['bias'] is not None: | ||
| bias = node.attributes['bias'].data.ravel() | ||
| assert len(bias) == n_out | ||
| out += bias | ||
| backend = model.config.get_config_value('Backend').lower() | ||
| assert backend in ('vitis', 'vivado') | ||
| comb = trace(inp, out) | ||
| node.attributes['da_kernel_cost'] = comb.cost | ||
| flavor = 'vitis' | ||
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| pragmas = ['#pragma HLS INLINE'] if flavor == 'vitis' else None | ||
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| fn_str, _ = hls_logic_and_bridge_gen(comb, fn_name, flavor, pragmas=pragmas, print_latency=True) | ||
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| io_type = node.model.config.get_config_value('IOType') | ||
| if io_type != 'io_parallel': | ||
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@@ -361,8 +361,9 @@ def match(self, node): | |
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| @requires('da') | ||
| def transform(self, model: 'ModelGraph', node: Layer): | ||
| from alkaid.cmvm import solver_options_t | ||
| from alkaid.codegen.hls import hls_logic_and_bridge_gen | ||
| from alkaid.trace import FVArray, trace | ||
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| kernel: np.ndarray = node.attributes['weight'].data | ||
| I, C, L_ker = kernel.shape | ||
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@@ -382,15 +383,15 @@ def transform(self, model: 'ModelGraph', node: Layer): | |
| _k, _i, _f = (v[i] for v in inp_kifs) | ||
| fn_name = f'einsum_{node.index}_da_{i}_of_{I}' | ||
| hard_dc = int(os.environ.get('DA_HARD_DC', 2)) | ||
| options: solver_options_t = {'hard_dc': hard_dc, 'search_all_decompose_dc': True} | ||
| inp = FVArray.from_kif(_k, _i, _f, solver_options=options) | ||
| out = inp @ kernel[i] | ||
| comb = trace(inp, out) | ||
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| node.attributes['da_kernel_cost'] += comb.cost | ||
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| pragmas = ['#pragma HLS INLINE'] if flavor == 'vitis' else None | ||
| fn_str, _ = hls_logic_and_bridge_gen(comb, fn_name, flavor, pragmas=pragmas, print_latency=True) | ||
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| fn_strs.append(fn_str) | ||
| fn_call = f'{fn_name}(&inp_tpose[({i} * {L_data} + l0) * {C}], &out_tpose[({i} * {L_data} + l0) * {L_ker}]);' | ||
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@@ -415,8 +416,9 @@ def match(self, node): | |
| return isinstance(node, DACombinational) | ||
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| def transform(self, model: 'ModelGraph', node: DACombinational): | ||
| from alkaid.codegen.hls import hls_logic_and_bridge_gen | ||
| from alkaid.codegen.hls.hls_codegen import get_io_types | ||
| from alkaid.trace import FVArrayInput, trace | ||
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| io_type = model.config.get_config_value('IOType') | ||
| if io_type != 'io_parallel': | ||
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@@ -428,14 +430,15 @@ def transform(self, model: 'ModelGraph', node: DACombinational): | |
| B, I, s = inp_p.width, inp_p.integer, inp_p.signed | ||
| i, f = I - s, B - I | ||
| comb = node.attributes['da_comb_logic'] | ||
| inp = FVArrayInput(comb.shape[0]).quantize(s, i, f) | ||
| out = comb(inp) | ||
| comb = trace(inp, out) | ||
| node.attributes['da_comb_logic'] = comb | ||
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| comb = node.attributes['da_comb_logic'] | ||
| backend = model.config.get_config_value('Backend').lower() | ||
| node.attributes['da_comb_logic'] = comb | ||
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| if backend in ('vitis', 'vivado'): | ||
| flavor = 'vitis' | ||
| elif backend == 'oneapi': | ||
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@@ -444,16 +447,44 @@ def transform(self, model: 'ModelGraph', node: DACombinational): | |
| raise ValueError(f'Unsupported backend {backend} for DACombinational layer.') | ||
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| fn_name = f'da_comblogic_{node.index}' | ||
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| out_t: str = node.get_output_variable().type.name | ||
| inp_name: str = ', '.join(node.inputs) | ||
| out_name: str = node.get_output_variable().name | ||
| namespace = model.config.get_writer_config().get('Namespace', None) or 'nnet' | ||
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| inp_names = node.inputs | ||
| inp_types = [model.graph[name].get_output_variable().type.name for name in inp_names] | ||
| inp_ts = ', '.join(inp_types) | ||
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| fn_name_internal = f'{fn_name}_internal' if len(inp_names) > 1 else fn_name | ||
| _name = fn_name_internal if len(inp_names) > 1 else fn_name | ||
| comb_logic, _ = hls_logic_and_bridge_gen( | ||
| comb, _name, flavor=flavor, pragmas=['#pragma HLS INLINE'], print_latency=True | ||
| ) | ||
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| # When there's multiple inputs, make a wrapper doing the concatenation and rename the original fn. | ||
| if len(inp_names) > 1: | ||
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Contributor
There was a problem hiding this comment. I think it would be good to add a test for the multi-input case.
Contributor
There was a problem hiding this comment. @calad0i can you comment why this case is not really reachable at the moment for the record? I'm fine with merging without the test, but would like to have it documented.
Contributor
Author
There was a problem hiding this comment. It can be reachable by any new layer implementation, like any transformer blocks. But those are already registered in hls4ml,and you need to patch to disable the current path to enable fallback. Or, some new layer is to be proposed and implemented. |
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| inp_sizes = [prod(model.graph[name].get_output_variable().shape) for name in inp_names] | ||
| template_args = ', '.join(f'typename inp{i}_t' for i in range(len(inp_names))) + ', typename out_t' | ||
| fn_args = ', '.join(f'inp{i}_t inp{i}[{s}]' for i, s in enumerate(inp_sizes)) + f', out_t out[{comb.shape[1]}]' | ||
| _inp_t_str = get_io_types(comb, 'vitis')[0] | ||
| forloop = """for (size_t i = {n}; i < {m}; i++) {{ | ||
| inp_buf[i] = inp{i}[i-{n}];}} | ||
| #pragma HLS UNROLL""" | ||
| N = [0] + np.cumsum(inp_sizes).tolist() | ||
| forloops = '\n '.join(forloop.format(i=i, n=N[i], m=N[i + 1]) for i in range(len(inp_names))) | ||
| wrapper_fn = f"""template <{template_args}> | ||
| void {fn_name}({fn_args}) {{ | ||
| {_inp_t_str} inp_buf[{comb.shape[0]}]; | ||
| #pragma HLS INLINE | ||
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| {forloops} | ||
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| {fn_name_internal}<{_inp_t_str}, out_t>(inp_buf, out); | ||
| }}""" | ||
| comb_logic = comb_logic + '\n\n' + wrapper_fn | ||
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| fn_cpp = f'{namespace}::{fn_name}<{inp_ts}, {out_t}>({inp_name}, {out_name});' | ||
| node.attributes['da_codegen'] = Source(comb_logic) | ||
| node.attributes['function_cpp'] = fn_cpp | ||
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Here you are dropping the
HWConfigargument. I see that the default in alkaid isHWConfig(1, 1, -1), so different from what was passed here before. Just checking if that is an intended change.There was a problem hiding this comment.
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This is intended; the parameter is used for alkaid internal surrogate, and the config changed